Podcasts about SpaCy

This Fri we gather together to honor our Homie Nicholas Barreto @cannabis.connoisseur.007 Nick was a tireless cannawarrior who battled for all of us on both state and national levels to educate people on why we need to deschedule and not reschedule cannabis. Nick literally lived , Breathed cannabis and cannabis reform unfortunately In his personal life Nick was also battling stage 4 Lymphatic Cancer and because he was such a vibrant person many people were unaware of his condition. He is in pain no more and his friends are gonna share some stories and take some huge dabs probably not as big as some of his cannabis Guinness world records that he loved to promote. His friends Spacy @lab_koat_genetics , @hashfight, Mike @f4nggggg , @takin.tops.official ,@terpicanaextracts and many more will share their personal Nick stories. We also have once again @weedshouldtastegood Miles in the studio ,and checking in we have our friends Mike @f4nggggg who is organizing @theorganiccup and Lance Lambert from @grovebags who is putting on another puffpuffpass event called “ le international puff puff primordial” we will see how that's going and get updates next week via @cannabisexperience. And finally we have Brian @boxbrown who was brought to our attention by Miles and I could tell after looking at his comics that we are all on the same page. Coming out of Pennsylvania. Brian is still deep in the East Coast cannabis world, but has his finger on the pulse everywhere from what I can see. Looks like he'll also be in Spain so lots of crossover. We are also all going out to see out friends @ed_rush and his partner @matt.optical So get that @dabx GO rig charged your @jerome_baker bong Clean with some ice

Back for another round of scents we've tried, perfumes we want, and releases that were just meh. C'est la vie as they say (but we would probably butcher in a very Texas way).Join us as we run down the fragrances we've been wearing and a very successful round of The Game.------Join us in Dallas at the Be Our Valentine event at the Dallas ContemporaryWe'll be joining Tony from Spacy for a Fragrance SwapWednesday, February 12, 20256:00 PM - 9:00 PM------Shoutout to Midnight Stinks - Read her writings, reviews, and follow her on social (if you aren't already).(06:40) - - A New Blind Buy (10:40) - - Want Lists and Reddit (14:28) - - Sampling Gold Smoke by Régime des Fleurs (18:48) - - What We've Been Wearing (plus the new Jil Sander) (35:30) - - Tea Update! (37:54) - - The Game Scents Mentioned in this Episode:Brut by Fabergé / Melt My Heart, Silence The Sea,  Dead of Night, Fall Into Stars, and A Fire Within by Strangelove NYC / Reve d'Ossian by Oriza L Legrand / Fille En Aiguilles by Serge Lutens / Incense Kyoto by Comme des Garçons / Imagination by Louis Vuitton / Le Lion by Chanel / Incense Rori by Amouage / Baudelaire by Byredo / Cannabis Santal by Fresh / Gold Smoke and Fleur Éclair by Régime des Fleurs / Jeux de Peau by Serge Lutens / Olympic Amber by Olympic Orchids / Duro by Nasomatto / Ani by Nishane / L'ame Perdue and L'Astre by Le Galion / Leaf, Coffea, Earth, Miel, Smoke, and Black Tea by Jil Sander / Gold by Perfumer H / Sunday Cologne by Byredo / Les Cahiers Secrets by Jouissance / Cuir Ottoman by Serge Lutens / Épices and Pistil by Miskeo / Ambre Sultan by Serge Lutens / Faunus by La Curie / Muguet Fleuri by Oriza L LegrandThe GameChypre-Mousse by Oriza L Legrand / La Liturgie des Heures by Jovoy / Musc Tonkin by Parfums d'Empire / Daim by Miskeo / Le Lion by Chanel / Ossuary by La CurieTeas Mentioned - Tea Odyssey by Byredo, Yorkshire by Perfumer H, Yorkshire Gold, Harney's Irish Breakfast Tea, and Russion Breakfast Tea by Mariage Frères.Please feel free to email us at hello@fragraphilia.com - Send us questions, comments, or recommendations. We can be found on TikTok and Instagram @fragraphilia

In the episode "S3Ep149G26 Demanding Better", retired Major General Brad Spacy joins hosts Dr. Dave Peltz and Dr. Joe Hamlett to explore the concept of “demanding better” as a critical element in personal, professional, and leadership development. Spacy emphasizes the importance of self-reflection, setting high standards, and leading by example to inspire growth in oneself and others. Drawing on his military background, he highlights accountability, clarity in expectations, and the need to eliminate "gray areas" as foundational for achieving excellence. The conversation also touches on how fostering a mindset that "anything is possible" motivates individuals to exceed expectations and create meaningful impact. The episode underscores the value of perseverance, structured self-improvement, and nurturing leadership in both military and civilian settings. JONSOP https://jonsop.com jonsopdr2@gmail.com Brad Spacy https://www.linkedin.com/in/bradley-spacy-a4620a203/ Dr. Joe Hamlett ⁠dr.joehamlett@gmail.com⁠ http:/www.leading2changeconsulting.com ⁠https://www.linkedin.com/in/drjoehamlett/⁠ http:/www.leading2changeconsulting.com/linkinbio/ Dr. Dave Peltz ⁠PeltzCS@outlook.com⁠ ⁠https://www.PeltzConsultingServices.com/services⁠ ⁠https://www.linkedin.com/in/dr-david-peltz-phd/⁠ https://tinyurl.com/DrDavePeltz Music intro & Outro provided by David T. Peltz, Owner of Peltz Tech Services, ⁠david@peltztech.com⁠ Copyright © 2024 - Journeys Of Not So Ordinary People, All Rights Reserved

Do you have text that you want to process automatically? Maybe you want to pull out key products or topics of conversation? Maybe you want to get the sentiment? The possibilities are many with this week's topic: NLP with spaCy and Python. Our guest, Vincent D. Warmerdam, has worked on spaCy and other tools at Explosion AI and he's here to give us his tips and tricks for working with text from Python. Episode sponsors Posit Talk Python Courses Links from the show Course: Getting Started with NLP and spaCy: talkpython.fm Vincent on X: @fishnets88 Vincent on Mastodon: @koaning Programmable Keyboards on CalmCode: youtube.com Sample Space Podcast: youtube.com spaCy: spacy.io Course: Build An Audio AI App: talkpython.fm Lemma example: github.com Code for spaCy course: github.com Python Bytes transcripts: github.com scikit-lego: github.com Projects that import "this": calmcode.io Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Hugo speaks with Ines Montani and Matthew Honnibal, the creators of spaCy and founders of Explosion AI. Collectively, they've had a huge impact on the fields of industrial natural language processing (NLP), ML, and AI through their widely-used open-source library spaCy and their innovative annotation tool Prodigy. These tools have become essential for many data scientists and NLP practitioners in industry and academia alike. In this wide-ranging discussion, we dive into: • The evolution of applied NLP and its role in industry • The balance between large language models and smaller, specialized models • Human-in-the-loop distillation for creating faster, more data-private AI systems • The challenges and opportunities in NLP, including modularity, transparency, and privacy • The future of AI and software development • The potential impact of AI regulation on innovation and competition We also touch on their recent transition back to a smaller, more independent-minded company structure and the lessons learned from their journey in the AI startup world. Ines and Matt offer invaluable insights for data scientists, machine learning practitioners, and anyone interested in the practical applications of AI. They share their thoughts on how to approach NLP projects, the importance of data quality, and the role of open-source in advancing the field. Whether you're a seasoned NLP practitioner or just getting started with AI, this episode offers a wealth of knowledge from two of the field's most respected figures. Join us for a discussion that explores the current landscape of AI development, with insights that bridge the gap between cutting-edge research and real-world applications. LINKS The livestream on YouTube (https://youtube.com/live/-6o5-3cP0ik?feature=share) How S&P Global is making markets more transparent with NLP, spaCy and Prodigy (https://explosion.ai/blog/sp-global-commodities) A practical guide to human-in-the-loop distillation (https://explosion.ai/blog/human-in-the-loop-distillation) Laws of Tech: Commoditize Your Complement (https://gwern.net/complement) spaCy: Industrial-Strength Natural Language Processing (https://spacy.io/) LLMs with spaCy (https://spacy.io/usage/large-language-models) Explosion, building developer tools for AI, Machine Learning and Natural Language Processing (https://explosion.ai/) Back to our roots: Company update and future plans, by Matt and Ines (https://explosion.ai/blog/back-to-our-roots-company-update) Matt's detailed blog post: back to our roots (https://honnibal.dev/blog/back-to-our-roots) Ines on twitter (https://x.com/_inesmontani) Matt on twitter (https://x.com/honnibal) Vanishing Gradients on Twitter (https://twitter.com/vanishingdata) Hugo on Twitter (https://twitter.com/hugobowne) Check out and subcribe to our lu.ma calendar (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) for upcoming livestreams!

In this episode we enlist in U.N. Spacy and face off against the Zentradi and Meltrandi as we protect the last hope for humanity in Super Dimension Fortress Macross: Do You Remember Love?Support this podcast at — https://redcircle.com/mmman/donations

There hasn't been a boom like the AI boom since the .com days. And it may look like a space destined to be controlled by a couple of tech giants. But Ines Montani thinks open source will play an important role in the future of AI. I hope you join us for this excellent conversation about the future of AI and open source. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Porkbun Talk Python Courses Links from the show Ines Montani on Twitter: @_inesmontani spaCy: spacy.io Prodigy App: prodi.gy Ines' presentation at PyCon Lithuania: youtube.com LM Studio: lmstudio.ai Little Bobby Tables: xkcd.com spaCy and NLP course: talkpython.fm Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to us on YouTube: youtube.com Follow Talk Python on Mastodon: talkpython Follow Michael on Mastodon: mkennedy

Topics covered in this episode: Git: Force push safely with --force-with-lease and --force-if-includes Thoughts from PyCon 2024 Being friendly: Strategies for friendly fork management tach Extras Joke Watch on YouTube About the show Sponsored by Mailtrap: pythonbytes.fm/mailtrap Connect with the hosts Michael: @mkennedy@fosstodon.org Brian: @brianokken@fosstodon.org Show: @pythonbytes@fosstodon.org Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesdays at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Git: Force push safely with --force-with-lease and --force-if-includes Adam Johnson Using gentle force Avoid stomping on remote changes with a couple extra flags. Michael #2: Thoughts from PyCon 2024 PyCon is special - the connections you make are always more than you expect Great to see many old friends Did a ”live” Talk Python episode that'll be out in a few weeks. The talks look great, we'll let you know when they land on YouTube. Masks were a mistake - universally heard complaints from fellow attendees. This is my two cents towards a more reasonable next PyCon. Brian #3: Being friendly: Strategies for friendly fork management That's part 2. Part 1 is Being friendly: Friendly forks 101 Lessley Dennington on GitHub Blog Examples of long running friendly forks git-for-windows/git, microsift/git, github/git two public, one private Fork management strategies - when pulling changes downstream merging rebase git-for-windows/git uses this proactively and regularly fake merge + rebase new branch microsoft/git uses this new branch from upstream major versions merge previous changes to new branch traditional merge github/git uses this, conservatively, after a few point bug fix versions Michael #4: tach A Python tool to enforce a modular, decoupled package architecture. tach allows you to define boundaries and control dependencies between your Python packages. Each package can define its public interface. If a package tries to import from another package that is not listed as a dependency, tach will report an error. If a package tries to import from another package and does not use its public interface, with strict: true set, tach will report an error. Zero runtime impact. Extras Brian: Logfire - new observability platform from the pydantic team - free for now Michael: 10% off the new spaCy course throughout May Joke: Evolution of smart products

Topics covered in this episode: I asked 100 devs why they aren't shipping faster. Here's what I learned Python 3.13.0 beta 1 released A theme editor for JupyterLab rich-argparse Extras Joke Watch on YouTube About the show Sponsored by Mailtrap: pythonbytes.fm/mailtrap Connect with the hosts Michael: @mkennedy@fosstodon.org Brian: @brianokken@fosstodon.org Show: @pythonbytes@fosstodon.org Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesdays at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: I asked 100 devs why they aren't shipping faster. Here's what I learned by Daksh Gupta (via PyCoders) What's stopping you from shipping faster? Dependency bugs Complicated codebase >There is so much undocumented in our service, including poor records of new features, nonexistent or outdated info on our dependencies, or even essential things like best practices for testing, a lot of time is wasted in syncs trying to find the right information QA Loops Waiting for spec > At Amazon? Meetings, approval, talking to 10 different stakeholders because changing the color of a button affects 15 micro services Writing tests Deployment/build speed Scope creep > The human tendency to stuff last-minute items into the crevices of their luggage minutes before leaving for the airport manifests itself at software companies as scope creep. Unclear requirements Excessive meetings Motivation >honest answer is i was on ads >and that's a very old / complicated / large stack (edited) >and i didn't understand it >my friends on younger teams seemed happier, i was miserable DORA metrics Brian #2: Python 3.13.0 beta 1 released "Python 3.13 is still in development. This release, 3.13.0b1, is the first of four beta release previews of 3.13.” New REPL, featuring multi-line editing, color support, colorized exception tracebacks Cool GIL, JIT, and GC features Typing changes, including typing.TypeIs . See last weeks episode and TypeIs does what I thought TypeGuard would do in Python Some nice dead battery removals and more But seriously, the REPL is cool. Just ask Trey The new REPL in Python 3.13 - Trey Hunner Michael #3: A theme editor for JupyterLab by Florence Haudin A new tool for authoring JupyterLab themes To lower the bar for customizing JupyterLab we created a new tool providing a simple interface for tuning the JupyterLab appearance interactively. See jupyterlab-theme-editor on github Brian #4: rich-argparse “Format argparse and optparse help using rich.” “rich-argparse improves the look and readability of argparse's help while requiring minimal changes to the code.” They're not kidding. 2 line code change. from rich_argparse import RichHelpFormatter parser = argparse.ArgumentParser(..., formatter_class=RichHelpFormatter) Extras Brian: pytest course is now switched to the new platform. I sent out an email including how to save their spot on the old site and mark that spot complete on the new site. There's now comments on the course now. Trying that out. If you've got a question, just ask in that section. Michael: A new Talk Python course: Getting Started with NLP and spaCy Joke: Testing holiday

Jan og Henrik snakker om Midt om natten, fra 1984. Kronologisk gennemgang af filmen. www.janoghenrik.dk/ www.henrikogjan.dk/ På Nørrebro i København har 24 unge hjemløse besat en saneringsmoden ejendom, politikeren Kaj Buhmann (Poul Bundgaard) fra det moderate folkeparti kræver lov og orden overholdt, og forlanger huset rømmet ved politiets hjælp, han forlanger BZerne sat ud. Politiets første besøg ender med, at den veltalende Arnold (Erik Clausen) taler dem ud af gaden, men de kommer talstærkt tilbage om natten. BZerne barrikaderer sig, og politiet må bruge vold for, at skaffe sig adgang til lejlighederne. Spacy (Allan Olsen) springer ud af vinduet, og kommer slemt til skade, hvad der får den aggresive Benny (Kim Larsen) til, at gå til angreb på politiet, da Benny vågner op på hospitalet får han, at vide af politiass. Kurtzen, at han kan takke guderne for, at den bajonet han brugte var rustet fast i skeden. Benny og Arnold møder nede i Sydhavnen en gammel original "Tusindfryd" (Holger Boland) og sammen opretter de den hyggelige minifristaden "Haveje", snart begynder andre hjemløse også at flytte derud. De fleste af vennerne fra Nørrebro er imellem indflytterne. Benny og Arnold ernærer sig med forefaldende "Måneskinsarbejde" for snart gud og hvermand, dertil har Arnold lejet en varevogn af Charles (Buster Larsen). Det nye samfund får dog hurtigt problemer, en flok rockere "Rotterne" dukker op og smadrer deres Cafe, og gennembanker beboerne. Episoden skaber uenighed mellem Arnold og Benny, der ikke er enige om metoden, hvordan de skal takle rockerne, hvis de dukker op igen. Venskabet må stå sin prøve. En TV reporter opsøger fristaden, og aftaler med Arnold om, at møde Buhmann i krydsild, Buhmann angriber "Haveje" og dens beboere for at være asociale og amoralske, og en hån mod ejendomsretten, Arnold giver ham ret, og med sin veltalenhed bringer han Buhmann på glatis. Købmandens datter Susan Himmelblå føler sig tiltrukket af Benny, og begynder at komme i "Haveje" trods faderens (Ove Sprogøe's) uvilje. Hun advarer dem, folk er begyndt at se skævt til dem, og en dag får de sidste advarsel, "Haveje" skal rømmes. Spacy dukker op, i rullestol, Arnold afviser ham, han vil kun bringe dem ballade for han er blevet narkoman, Tusindfryd tilbyder, at han kan bo hos ham, dagen efter er han død af overdosis. Benny, Knold og et par stykker mere bortfører Buhmann og holder ham fangen i en gammel bunker, og da Arnold prøver at tale dem til fornuft, går Benny amok, og kommer i åbent håndgemæng med ham. Tekst fra danskefilm.dk

On this episode we talk about the new LEGO City Space set coming April 1st! Click here to purchase in the US!Enjoying the show...give us a like and comment on all platforms. Help us make the LEGO world available to all!Find us everywhere through LinkTreeMusic: www.bensound.comLEGO, the LEGO logo, the Minifigure, and the Brick and Knob configurations are trademarks of the LEGO Group of Companies. ©2024 The LEGO Group.THE BRICKS KING PODCAST IS NOT ENDORSED BY THE LEGO GROUP OR AFFILIATED IN ANY WAY.Become a supporter of this podcast: https://www.spreaker.com/podcast/the-bricks-king-podcast-lego--4920139/support.

Join the Oak Cliff Cultural Center for a conversation Tony Nguyen - the founder of Spacy, a 35-seat microcinema in Dallas, TX. In our twentieth episode, we talk with Tony about his vision for Spacy and his thoughts on arts and culture. OCCC is a division of the City of Dallas Office of Arts and Culture. The views and opinions expressed here are of the individuals only and do not necessarily reflect views or positions of the City of Dallas, the Office of Arts and Culture, or the Oak Cliff Cultural Center. Intro + Outro royalty free music prod. by Danya Vodovoz of VMP

Former KISS guitarist, the legendary Ace Frehley is makin' the rounds in support of his upcoming new album, "10,000 Volts", and he gave us a call this morning to talk about it....and, well...either it was late night or an early morning...or both...for ol' Ace....but who cares? It's fucking Ace Frehley!!!!

Giving us our next mix of the series we have Joakim, sending us blends of techno and psychedelic trance, enjoy! -- Follow @joakim-moe https://www.instagram.com/_joakimm_ -- "Joakim, a producer and dj located out of Santa Barbara, CA. Origins started with house and techno however throughout the years, dived deep and focuses more on the psychedelic trance style. Spacy atmospheres, driving basslines, and hypnotic melodies. Joakim's goal through each set is to bring the energy and frequency to a deeper level for the viewers and dancers to tap into and experience. While his sound is mostly linked to Psytrance, he still has a love for all genres :) Joakim is also one of the founders of Global Ascension Collective, an underground family music and arts festival. Originally started throwing renegades in the Mountains of Santa Barbara, but more recently have put on 3 day intimate gatherings out in the Socal Desert, with a mission of creating transformative experiences through the music, art, nature and other healing workshops!" -- Tracklist: 1. Marbs Rinzen Evan Casey - Torus 2. Marbs Rinzen Evan Casey - Ark 3. Sammy Legs Malley - Telescope 4. Sammy legs - Mad Psyentist 5. Electrypnose, Kari - Pazar 6. Evil Oil Man - The Zero Theorem 7. Protonica, Airi, - Symmetry 8. Joakim - Contacting Aliens (Original Mix) 9. Joakim - Hypno Therapy (Original Mix) 10. Joakim - Power of the Magic Mushroom ( Original Mix ) 11. Flexus, Estefano haze , Stayos - Adventure 12. Atacama, Jakaan - Land of the fairies 13. Atacama, Jakaan - Complete Awakening 14. Atacama - Meteor 15. Module Virus, Ingrained Instincts - Experiences 16. Kromagon, Irukanji, Eleexr, eXoLT - Heacy Lohic

This episode features co-founder and CEO of Explosion, Ines Montani. Listen in as we discuss the evolution of the web and machine learning, the development of SpaCy, Natural Language Processing vs. Natural Language Understanding, the misconceptions of starting a software company, and so much more! Ines is a software developer working on Artificial Intelligence and Natural Language Processing technologies.She's the co-founder and CEO of Explosion, the company behind SpaCy, one of the leading open-source libraries for NLP in Python and Prodigy, an annotation tool to help create training data for Machine Learning Models. Ines has an academic background in Communication Science, Media Studies and Linguistics and has been coding and designing websites since she was 11. She's been the keynote speaker at Python and Data Science conferences around the world.Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts.Listen on YouTube: https://youtu.be/XNFqFT-DZwo?si=Aj75TmsCyBQTyWqqListen on your favorite podcast platform:https://rss.com/podcasts/learning-from-machine-learning/1190862/References in the Episodehttps://explosion.ai/https://spacy.io/https://ines.io/Applied NLP ThinkingInes Montani - How to Ignore Most Startup Advice and Build a Decent Software Business Ines Montani: Incorporating LLMs into practical NLP workflowsInes Montani (spaCy) - Large Language Models from Prototype to Production [PyData Südwest] Confectionhttps://github.com/explosion/confectionResources to learn more about Learning from Machine Learninghttps://www.linkedin.com/company/learning-from-machine-learninghttps://mindfulmachines.substack.com/https://www.linkedin.com/in/sethplevine/https://medium.com/@levine.seth.p

This episode dives into the multifaceted realm of Natural Language Processing (NLP) with a guest expert, Ines Montani (#). The discussion revolves around the use of Python in the context of NLP, the complexities of language, the design of label schemes, and how educators and students can dive into this intriguing area. The conversation also touches on tools such as Prodigy (https://prodi.gy/) and Spacy (https://spacy.io/), as well as practical applications, including a humorous digression on the popular game, Fortnite (https://www.epicgames.com/fortnite/). Teachers are encouraged to explore NLP with their students, emphasizing the importance of hands-on experience and data annotation. There's also a mention of a fascinating project involving a "magic mirror (https://www.raspberrypi.com/tutorials/how-to-build-a-super-slim-smart-mirror/)" powered by Raspberry Pi (https://www.raspberrypi.org/). Special Guest: Ines Montani.

Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training Test & Code Podcast Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org Brian: @brianokken@fosstodon.org Show: @pythonbytes@fosstodon.org Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesdays at 11am PT. Older video versions available there too. Michael #1: Pydantic v2 released Pydantic V2 is compatible with Python 3.7 and above. There is a migration guide. Check out the bump-pydantic tool to auto upgrade your classes Brian #2: Two Ways to Turbo-Charge tox Hynek Not just tox run-parallel or tox -p or tox --``parallel , but you should know about that also. The 2 ways Build one wheel instead of N sdists Run pytest in parallel tox builds source distributions, sdists, for each environment before running tests. that's not really what we want, especially if we have a test matrix. It'd be better to build a wheel once, and use that for all the environments. Add this to your tox.ini and now we get one wheel build [testenv] package = wheel wheel_build_env = .pkg It will save time. And a lot if you have a lengthy build. Run pytest in parallel, instead of tox in parallel, with pytest -n auto Requires the pytest-xdist plugin. Can slow down tests if your tests are pretty fast anyway. If you're using hypothesis, you probably want to try this. There are some gotchas and workarounds (like getting coverage to work) in the article. Michael #3: Awesome Pydantic A curated list of awesome things related to Pydantic!

Free Platforms and Libraries for Quantum Machine LearningOpen AI introduces "SuperAlignment"Sam Altman says the worst-case scenario for artificial intelligence is 'lights out for all of us'Ai deals with Climate ResearchGrammy to Establish a Nomination for Songs Created by AIGoogle releases "Help Me Write" AI for your GmailGenerative AI in games will create a copyright crisisAs AI cheating booms, so does the industry detecting it: ‘We couldn't keep up with demand'Urtopia unveils an e-bike with ChatGPT integrationJapan emphasizing the importance of students understanding AiThis podcast is generated using the Wondercraft AI platform, a tool that makes it super easy to start your own podcast, by enabling you to use hyper-realistic AI voices as your host. Like mine!Attention AI Unraveled podcast listeners!Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book "AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence," by Etienne Noumen, now available at Apple, Google, or Amazon today!Amplify Your Brand's Exposure with the AI Unraveled Podcast - Elevate Your Sales Today at: https://djamgatech.com/product/ai-unraveled-podcast-per-episode-integration/

No More Small Businesses With Ai5/11/23IBM Reportedly Prepares to Halt Hiring for Jobs That Could Soon Be Replaced by AIhttps://www.youtube.com/watch?v=Yfn01Z1ZUbcIBM CEO Arvind Krishna Says A.I. Will Make More Jobs Than it Will Replacehttps://www.youtube.com/watch?v=EUX7DkQrVyYRhinoLeg 50 Job Titles with Descriptionshttps://s3.us-central-1.wasabisys.com/rhinoleg-backoffice/Docs/RhinoLeg50.docxIn the next 36 months, about 8,000 jobs will be replacedCEOs want 2 things:MoneyMore moneyPrompt 1Antonio T Smith Jr is teaching a class right now. He asked me to tell you to give him 50 jobs that artificial intelligence will replace in the next 5 years. I need a list of 50 and you don't have to give me a description, just job, title, or industry is good enough. ChatGPT ResponseData Entry ClerkTelemarketerBookkeeping ClerkCompensation and Benefits ManagerReceptionistCourierProofreaderRetail SalespersonMarket Research AnalystAdvertising Sales AgentTaxi DriverFast Food CookInsurance UnderwriterTravel AgentReal Estate BrokerToll Booth OperatorInventory ManagerParalegalBank TellerAgricultural WorkerCustomer Service RepresentativeCredit AnalystLoan OfficerCashierAssembly Line WorkerTextile WorkerHotel Desk ClerkLibrarianPostal Service WorkerNewspaper DeliverySports RefereeTrain OperatorMeter ReaderMail SorterWarehouse LaborerTranscriptionistPhoto ProcessorParking Enforcement WorkerConstruction LaborerSurveyorAircraft Cargo Handling SupervisorSecurity GuardFarm Labor ContractorFilm and Video EditorInsurance Claims ClerkOrder ClerkPayroll and Timekeeping ClerkProcurement ClerkShipping and Receiving ClerkSwitchboard OperatorPrompt 2Antonio T Smith Jr told me the 8,000 jobs will be replaced in the next 5 years by Ai. Please analyze this list and give me another 50 regardless of industry that do not repeat this list.ChatGPT ResponseMedical TranscriptionistSewing Machine OperatorGaming DealerPharmacy TechnicianHousekeeping CleanerBaristaLegal SecretaryReservation AgentFast Food ServerBartenderTaxi DispatcherCasino DealerPrint Binding and Finishing WorkerWatch RepairerInsurance Policy Processing ClerkMail CarrierLibrary TechnicianData AnalystReport WriterOffice ClerkTelemarketing Sales AgentBudget AnalystAccountantBank Loan OfficerDesktop PublisherIT Support SpecialistTechnical WriterStock ClerkHR AssistantSales RepresentativeLogisticianPurchasing AgentCost EstimatorReal Estate AppraiserFinancial AnalystTax PreparerBill and Account CollectorSewers, HandPhotographic Process WorkerNew Accounts ClerkBrokerage ClerkOrder FillerFile ClerkInsurance AppraiserFreight AgentRadio OperatorAvionics TechnicianClinical Laboratory TechnicianNuclear Power Reactor OperatorOccupational Therapy AidePrompt 3I am a CEO of a small business, and I am looking to replace 500 jobs in my industry with Ai within the next year. My industry is [your industry], and I am giving you a list of 10 job titles. Please analyze this list of 10 and tell me how these job titles can be maximized to increase my bottom line by $100,000 a year. With each job title and each analysis, give me a short description of how you would use this job title in my industry, which is [your industry]. It is important that you use my industry. ChatGPT ResponseData Entry Clerk: In an AI-powered CRM industry, this role could be replaced or enhanced by automated data entry systems. This could save time, reduce human error, and allow for real-time data updates. The cost savings from minimizing errors and accelerating the data entry process could significantly contribute to your bottom line.Telemarketer: AI can automate certain tasks of a telemarketer, such as scheduling calls and following up with leads. Automated voice systems or chatbots can handle initial customer interactions, filtering out uninterested leads and saving human agents for more promising or complex cases. This increases efficiency and reduces costs.Bookkeeping Clerk: AI systems can handle a lot of the routine tasks of a bookkeeping clerk, such as invoicing, payroll, and financial reporting. This reduces the likelihood of errors and frees up time for your financial team to focus on more strategic tasks, like financial planning and analysis, which could improve your bottom line.Compensation and Benefits Manager: AI can automate and streamline many aspects of compensation and benefits management, such as tracking employee performance, determining appropriate compensation, and managing benefits enrollment. This allows for more accurate and efficient management of employee compensation, which can help retain top talent and improve overall productivity.Receptionist: AI can replace or assist receptionists through automated scheduling systems, call forwarding, and visitor registration. An AI virtual assistant can handle inquiries 24/7, improving customer service and freeing up human staff for higher value tasks.Courier: In an AI-powered CRM business, physical courier roles may be limited. However, if documents or hardware need to be moved, drones or automated delivery systems could be used to reduce costs and speed up delivery times.Proofreader: AI can automate many aspects of proofreading, such as grammar, spelling, and punctuation checks. This could improve the quality of your content and communication, enhancing your brand image and customer satisfaction.Retail Salesperson: In the context of AI-powered CRM, an AI sales assistant could replace or enhance the role of a retail salesperson. It can provide customers with personalized product recommendations, manage inventory, and process orders. This can lead to increased sales and customer satisfaction.Market Research Analyst: AI can automate and enhance many aspects of market research. It can analyze large data sets to identify trends, segment customers, and predict future behavior. This can provide more accurate and timely insights, leading to better decision-making and strategy development.Advertising Sales Agent: AI can automate many aspects of advertising sales, such as identifying potential clients, personalized outreach, and tracking campaign performance. This can increase advertising sales efficiency and effectiveness, leading to increased revenue and profits.Taxi Driver: In the context of your industry, this might relate to the delivery of services or products. Automated scheduling and routing could optimize the efficiency of these deliveries, reducing costs. Moreover, your CRM could potentially offer solutions to taxi companies, providing AI-powered dispatching and routing, leading to increased sales and profits.Fast Food Cook: This could relate to the execution of routine tasks in your business. AI can automate repetitive tasks, improving efficiency and reducing errors. In terms of your CRM, if it's catering to fast food chains, an AI-powered system could predict order volumes and optimize food preparation times, enhancing customer satisfaction and sales.Insurance Underwriter: AI can automate the risk assessment and policy pricing, improving efficiency, accuracy, and consistency. This would save time and reduce costs in similar risk evaluation tasks in your business. For the CRM aspect, AI could offer predictive analytics and automated risk assessment features to insurance firms, enhancing your product offering.Travel Agent: An AI system can automate and personalize recommendations, bookings, and customer service, improving customer satisfaction and sales. Within your CRM, this could translate to providing intelligent recommendations and automating follow-ups with leads and customers.Real Estate Broker: AI can automate property matching, price estimation, and lead generation, improving efficiency and effectiveness. Similarly, your CRM could potentially offer features tailored to the real estate industry, such as automated property matching and personalized client follow-ups.Toll Booth Operator: This might relate to payment collection within your business. AI can automate payment processing and fraud detection, reducing costs and improving cash flow. As for your CRM, you could offer automated payment processing and fraud detection features to clients in industries that collect tolls or fees.Inventory Manager: AI can automate and optimize inventory management, reducing costs and improving service levels. This could translate into your CRM offering AI-powered inventory management solutions, potentially increasing sales and customer satisfaction.Paralegal: AI can automate legal research and document review, improving efficiency and accuracy. Within your CRM, this could mean providing intelligent search and document analysis features, making it more attractive to legal firms.Bank Teller: AI can automate transactions, customer service, and fraud detection, improving efficiency and security. Your CRM could potentially offer tailored solutions to banks, such as AI-powered customer service and fraud detection features.Agricultural Worker: AI can automate crop monitoring, irrigation, and harvesting, improving efficiency and yields. This might mean that your CRM could offer features like predictive analytics and automated task management to clients in the agricultural industry.Customer Service Representative: AI chatbots could handle common inquiries, freeing up human agents to handle more complex queries. This reduces costs and potentially improves customer satisfaction. You could also offer an AI chatbot feature in your CRM, enhancing your product offering.Credit Analyst: AI algorithms can automate credit risk assessment, reducing errors and bias. This could translate to cost savings in any financial risk evaluation within your company. For your CRM, you might offer AI-powered credit risk assessment features to clients in the financial sector.Loan Officer: AI can automate the loan application and approval process, improving efficiency and reducing bias. Similarly, you could offer AI-powered loan processing features in your CRM, making it more attractive to banking and finance clients.Cashier: In a CRM company, this could relate to payment processing. AI can automate payment collection and fraud detection, reducing costs and improving cash flow. You could also offer these features in your CRM to clients in retail and other sectors.Assembly Line Worker: This might relate to the execution of repetitive tasks in your company. AI can automate such tasks, improving efficiency and reducing errors. Your CRM could potentially offer workflow automation features to clients in manufacturing and other sectors.Textile Worker: This could relate to quality control in your company. AI can automate quality checks, improving consistency and reducing costs. Similarly, your CRM could offer AI-powered quality control features to clients in the textile and other industries.Hotel Desk Clerk: AI can automate booking and customer service tasks, improving efficiency and customer satisfaction. Within your CRM, you might offer AI-powered booking and customer service features to clients in the hospitality sector.Librarian: In a CRM context, this could relate to data management. AI can automate data categorization and retrieval, reducing errors and saving time. Your CRM could also offer AI-powered data management features, enhancing its appeal to various sectors.Postal Service Worker: This could relate to the delivery of services or products in your company. AI can automate scheduling and routing, improving efficiency. Similarly, your CRM could offer AI-powered delivery scheduling features to clients in logistics and other sectors.Newspaper Delivery: In a digital context, this could relate to the distribution of content or information. AI can automate content distribution based on customer preferences and behavior, improving engagement. Your CRM could offer AI-powered content distribution features, enhancing its appeal to media and other sectors.Sports Referee: This could relate to quality assurance within your company. AI can automate checks and balances, reducing errors and maintaining high quality. In terms of your CRM product, you could offer AI-based quality assurance features for clients that need to enforce rules or standards.Train Operator: In a metaphorical sense, this could relate to running operations smoothly. AI can automate and optimize various operational tasks, improving efficiency and reducing costs. Moreover, your CRM could offer AI-based operations management features for industries that have complex scheduling or routing needs.Meter Reader: This could relate to the measurement and analysis of business metrics in your company. AI can automate data collection, analysis, and reporting, improving accuracy and speed. Your CRM could offer similar features to clients who need to regularly track and analyze data.Mail Sorter: This could relate to sorting and managing incoming customer inquiries or requests. AI can automate this process, ensuring inquiries are promptly and appropriately addressed. This feature could also be included in your CRM for clients that receive high volumes of customer communication.Warehouse Laborer: This might relate to the management of digital resources in your company. AI can automate the organization and retrieval of these resources, improving efficiency. This kind of resource management feature could be valuable in your CRM for clients that manage large amounts of digital assets.Transcriptionist: AI can automate transcription, saving time and improving accuracy. This could be useful within your company for transcribing meetings or customer calls. Similarly, a transcription feature in your CRM could be valuable to clients that record customer calls or meetings.Photo Processor: In the context of your business, this might relate to processing and analyzing visual data. AI can automate this, providing insights more quickly and accurately. This feature could also be included in your CRM for clients that use visual data in their business.Parking Enforcement Worker: This could relate to enforcing rules and standards in your company. AI can automate monitoring and enforcement, reducing errors and improving compliance. A rule enforcement feature in your CRM could be valuable to clients that need to monitor compliance with certain standards or regulations.Construction Laborer: This could relate to building and maintaining your CRM platform. AI can automate many aspects of software development and maintenance, improving efficiency and reducing errors. This could result in cost savings and more rapid product updates.Surveyor: This might relate to understanding customer needs and preferences. AI can automate survey distribution, response collection, and data analysis, providing insights more quickly and accurately. An AI-powered survey feature in your CRM could be valuable to clients that want to better understand their customers.Aircraft Cargo Handling Supervisor: This role can be analogous to managing the flow of data or services in your business. AI can automate and optimize the handling of data or services, improving efficiency and reducing costs. Your CRM could also offer AI-powered data management and optimization features to clients in industries that handle large amounts of data or services.Security Guard: This could relate to security measures within your digital systems. AI can enhance security by automating threat detection and response, reducing the risk of data breaches and improving trust. Your CRM could offer similar AI-powered security features to clients, enhancing its appeal.Farm Labor Contractor: In a metaphorical sense, this could relate to managing resources in your company. AI can automate resource allocation and optimization, improving efficiency. Your CRM could offer similar AI-powered resource management features to clients, particularly in industries that require resource optimization.Film and Video Editor: This could be related to the editing and customization of user interfaces or customer experiences. AI can automate the customization of interfaces or experiences based on user preferences and behavior, improving customer satisfaction. Your CRM could offer similar AI-powered customization features, enhancing its appeal.Insurance Claims Clerk: AI can automate the processing and evaluation of claims (or in your company's context, complaints or requests), improving efficiency and accuracy. Your CRM could offer similar AI-powered features to clients in the insurance sector, making it more attractive to them.Order Clerk: This could relate to the management of customer orders or requests in your company. AI can automate the processing and tracking of orders or requests, improving efficiency and customer satisfaction. Your CRM could offer similar AI-powered order management features to clients.Payroll and Timekeeping Clerk: AI can automate payroll and timekeeping tasks, reducing errors and saving time. This could result in cost savings for your company and could also be a feature in your CRM for clients who need to manage payroll and timekeeping.Procurement Clerk: This could be related to the procurement of services or digital resources in your company. AI can automate and optimize procurement processes, reducing costs and improving efficiency. Your CRM could offer similar AI-powered procurement features to clients, especially those in industries that involve complex procurement processes.Shipping and Receiving Clerk: In a digital context, this might relate to the sending and receiving of data or services. AI can automate and optimize these processes, improving efficiency and reducing errors. Your CRM could offer similar AI-powered features to clients in industries that involve the exchange of large amounts of data or services.Switchboard Operator: This could relate to the routing of customer inquiries or requests in your company. AI can automate the routing of inquiries or requests, ensuring they are promptly and appropriately addressed. Your CRM could offer similar AI-powered routing features to clients who receive a high volume of customer communication.Prompt 4Antonio wants me to build the payroll department, but he doesn't want any employees. He wants it to be completely Ai driven. With that being said, I want you to give a 25-step process to build an Ai driven payroll and timekeeping department for the Ai driven CRM company. It is vital that these 25 steps are not general steps as Antonio and the company has the resources such as machine learning, deep learning, and Ai skills to pull this off. Your task is two-fold. First, give me the first 25 steps. Second is to teach me and give me the tools at your disposal to accomplish these 25 steps.ChatGPT Response1. Define Your Objectives: Clearly outline the tasks you want the AI to perform, such as calculating wages, making payments, tracking working hours, handling tax deductions, and generating reports.2. Understand Regulatory Requirements: Research and understand the legal and regulatory requirements for payroll and timekeeping in the jurisdictions where your employees are located.3. Gather Data: Collect historical payroll and timekeeping data. This data will be used to train your AI models.4. Clean the Data: Preprocess the data to handle missing values, outliers, and inconsistencies.5. Data Analysis: Perform exploratory data analysis to understand patterns and relationships in your data.6. Feature Engineering: Identify relevant features in your data that the AI models can use to make accurate predictions.7. Define AI Model Metrics: Define the metrics you'll use to evaluate your AI models, such as accuracy, precision, and recall.8. Split the Data: Split your data into training, validation, and testing sets.9. Choose Model Architectures: Select appropriate machine learning or deep learning architectures for your models. For example, you might use a combination of regression models for wage calculations and classification models for regulatory compliance.10. Train Models: Use your training data to train your models.11. Validate Models: Use your validation data to tune your models and select the best performing ones.12. Test Models: Use your testing data to evaluate your models' performance. Make sure they meet the metrics you defined earlier.13. Interpret Models: Use techniques like SHAP or LIME to understand how your models are making decisions. This is especially important for compliance with regulations that require explainability in AI.14. Set up an AI Infrastructure: Set up the hardware and software infrastructure needed to run your AI models in production.15. Automate Data Pipelines: Build automated data pipelines to preprocess new payroll data and feed it into your AI models.16. Integrate Models into Infrastructure: Integrate your AI models into your infrastructure.17. Develop User Interfaces: Develop user interfaces that allow users to input data and retrieve outputs from your AI system.18. Implement Security Measures: Implement measures to protect sensitive payroll data, such as encryption and access controls.19. Build a Monitoring System: Build a system to monitor your AI models' performance over time.20. Test the Entire System: Conduct end-to-end testing of your entire AI system.21. Plan for Maintenance: Plan for ongoing maintenance of your AI system, including regular updates to your models and infrastructure.22. Train Employees: Train any remaining employees who will interact with the AI system on how to use it.23. Deploy the AI System: Deploy your AI system in production.24. Monitor and Update the System: Regularly monitor your AI system's performance and make updates as needed.25. Continual Learning: Implement a system for your AI models to continually learn from new data and improve over time.These steps may require a variety of tools and skills, including knowledge of machine learning and deep learning, proficiency in programming languages like Python or R, experience with AI frameworks like TensorFlow or PyTorch, and understanding of data management tools like SQL. You may also need to use cloud platforms like AWS, Google Cloud, or Azure for your AI infrastructure, and data visualization tools like Tableau or PowerBI for data analysis and model interpretation. Prompt 5I am ready to build this: Define Your Objectives: Clearly outline the tasks you want the AI to perform, such as calculating wages, making payments, tracking working hours, handling tax deductions, and generating reports. Please analyze my list below and build out my logic and basic implementation to have Ai accomplish all I have listed. Please make any improvements as needed.ChatGPT ResponseTo build an AI solution that can handle the tasks you outlined, we need to break down each task into sub-tasks and identify where AI can be most beneficial. Here's an AI-based implementation plan based on your list:1. Monitor Staff Attendance DailyUse facial recognition or biometrics for attendance tracking. AI can process this data in real-time.Anomaly detection algorithms can identify any unusual patterns in attendance, such as frequent late arrivals or absences.2. Collect, Review, and Verify Timesheets and Work ChartsNatural Language Processing (NLP) can be used to interpret unstructured timesheet data.Machine Learning (ML) algorithms can cross-verify timesheet data for accuracy, comparing it against project timelines, expected work hours, etc.3. Prepare Monthly Salary SheetsML algorithms can automatically calculate monthly salaries based on the data collected, including adjustments for PTO, overtime, bonuses, etc.4. Process Payroll Paperwork for New EmployeesOptical Character Recognition (OCR) and NLP can process and digitize paperwork, extracting relevant information to be added to the payroll system.5. Update HRIS and Payroll DataAutomate updates to HRIS using AI. Any changes in employee status trigger automatic updates in the payroll system.6. Stay Updated on Payroll-related LawsAI can be used to constantly scan and analyze legal databases and websites for updates to laws and regulations, alerting you when relevant changes occur.7. Provide Information on Payroll Policies and BenefitsAn AI chatbot can be programmed to answer common employee queries related to payroll and benefits.8. Investigate and Resolve Payroll and Timekeeping ComplaintsAI can assist in initial complaint triage, identifying the nature of the issue and routing it to the appropriate department or person for resolution.9. Prepare Month-end and Year-end ReportsAI can automate the generation of financial reports by pulling data from the payroll system and applying the necessary calculations and formatting.10. Maintain and Update Employee Information in Payroll RecordsAI can automatically update employee records whenever a change is detected (e.g., address change, bank account change, etc.).To accomplish this, you'll need a diverse set of tools and technologies, including machine learning and deep learning libraries (like scikit-learn, TensorFlow, PyTorch), NLP libraries (like NLTK, spaCy), OCR tools (like Tesseract), AI platforms (like IBM Watson, Google AI), and more. Programming languages like Python and R will also be essential.Remember, while AI can automate many tasks, it's crucial to have a human in the loop for supervision, especially for tasks involving sensitive personal data.Prompt 6What else can we add to this to improve our Ai capabilities?Closing ThoughtsWhen doing all of this, don't look at the progress for a year; just keep showing upVertical leadership/ownership takes timeYou are the greatest thing that happened to this planetThere is nothing more sacred to this world than youThere is nothing on this planet that God loves more than youIf you're not living your dreams, you must learn to lean in and give into the presentFall in love with losing as fast as possibleSupport this podcast at — https://redcircle.com/the-secret-to-success/exclusive-contentAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Are you feeling spacey or suffering from brain fog? You may have symptoms like mental fatigue, sleepiness, or forgetfulness. Brain fog or sluggish cognitive function can happen for lots of reasons. Research has shown that aging, traumatic brain injuries, central nervous system injuries- even dehydration can bring it on. Health conditions like lupus, celiac disease, chronic fatigue syndrome and Covid 19 can all cause temporary mental fogginess. People with ADHD sometimes experience brain fog, too. Dr. Mona Lisa shares her views on the connection between ADHD and that spacy feeling.  Would you like to be a part of the show? Join the studio audience during the live taping on Zoom. Register in advance for this meeting: ZOOM link after registering you will receive a confirmation email containing information about joining the meeting. During the show, you may even get through on the reading line (207 846 6475) for a free mini-reading. Dr. Mona Lisa's Private Readings Dr. Mona Lisa's Annual Medical Intuition Certificate Training Class - Get info https://www.drmonalisa.com/Summer_Camp_Maine_2023 Readings are educational only and are not intended to provide a physician-patient relationship, give diagnoses, prescribe treatment or do psychotherapy. Please contact your health care provider to obtain treatment. If you are in an emergency, please go to your nearest emergency room.*** Find books from Dr. Mona Lisa Schulz and other MindBodySpirit.fm podcast hosts in the online store Learn more about your ad choices. Visit megaphone.fm/adchoices

NLP practitioners: this episode is for you. From the awareness of linguistic elements and annotation to getting the necessary people in the room, Vincent Warmerdam presents to Jon Krohn a recipe for a successful project and the open-source NLP tools to get there. This episode is brought to you by epic LinkedIn Learning instructor Keith McCormick (https://linkedin.com/learning/instructors/keith-mccormick). Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information. In this episode you will learn: • How Vincent came to work with De Speld [08:57] • Vincent's role at Explosion [18:59] • How users can apply spaCy [21:46] • Prodigy: Annotate training data more efficiently with scripts [26:28] • How to manage “skill anxiety” with Calmcode [32:32] • How Vincent fixed bad labels [42:47] • The value of understanding linguistics for NLP [54:42] • How to constrain artificial stupidity [1:02:38] Additional materials: www.superdatascience.com/659

Oh my stars!  This week, we get totally far out as we discuss some of our top songs of the era with a space theme.  It's totally out of this world. We cover a lot of ground...or space, as the case may be, since we determined that we've traveled hundreds of thousands of miles just during the course of the episode.  Yes, that's just one of several space facts that you will learn.  Also: We celebrate 50,000 downloads and counting!  Wiggy Rockstone!  Jean Nate'! All sorts of Kellies!  Peanut butter!  Trippy kids' TV (and the resulting crushes)!  Revolutionary teenage bands!  Gramps is no longer unemployed!  AI song lyrics!  John Taylor clones!   So hop on the O3L rocketship and let's gooooooooooo!

Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts. This episode we welcome Vincent Warmerdam, creator of calmcode, and machine learning engineer at SpaCy to discuss Data Science, models and much more. @learningfrommachinelearningResources to learn more about Vincent Warmerdam:https://calmcode.io/https://youtu.be/kYMfE9u-lMohttps://youtu.be/S7vhi6RjBZAhttps://github.com/koaningReferences from the Episode:You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place https://amzn.to/3Jt1qjXThe Future of Operational Research is Past https://ackoffcenter.blogs.com/files/the-future-of-operational-research-is-past.pdfSupervised Learning is great - it's data collection that's broken https://explosion.ai/blog/supervised-learning-data-collectionDeon - An ethics checklist for data scientists https://deon.drivendata.org/Hadley Wickham - https://hadley.nz/Katharine Jarmul - https://www.linkedin.com/in/katharinejarmul/?originalSubdomain=deVicki Boykis - https://vickiboykis.com/Brett Victor - https://youtu.be/8pTEmbeENF4Resources to learn more about Learning from Machine Learning:https://www.linkedin.com/company/learning-from-machine-learning/https://www.linkedin.com/in/sethplevine/https://medium.com/@levine.seth.p

Twitter zaradi prevzema Elona Muska izgublja uporabnike in prešteva kritike strokovnjakov, zaposlenih in tviterašev. Kakšno je razpoloženje na slovenskem delu Twitterja, koliko uporabnikov še tvita v slovenščini, o čem govorijo in kako natančno lahko na podlagi Twitterja napovemo volilne rezultate? Omrežju Mastodon, ki velja za alternativo Twitterju, se je do sredine novembra pridružilo milijon uporabnikov, število narašča, povprečje dnevnih uporabnikov se povečuje. Marko Plahuta je programer, ki se ukvarja s strojnim učenjem na področju obdelave jezika. Z raziskovanjem in vizualizacijo se ukvarja v prostem času. Zapiski:  About the author - Virostatiq CENTER ZA JEZIKOVNE VIRE IN TEHNOLOGIJE Filmski pojmovnik – Slovenska kinoteka Kviz! Kaj Marko uporablja: Elastic Search za shranjevanje, iskanje in preproste agregacije Twitterjev API za zajemanje podatkov s Twitterja Naučene jezikovne modele, dostopne na HuggingFace, kot osnovo za klasifikatorje in generativne modele To zgoraj skupaj s knjižnicami TensorFlow/Keras in PyTorch spaCy, ki je nedavno izšel za slovenščino Classla, ki je podoben spaCyju, a temelji na Stanfordovi tehnologiji Starejše jezikovne tehnologije, zbrane v knjižnicah Gensim in Scikit-Learn UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction — umap 0.5 documentation GitHub - facebookresearch/faiss: A library for efficient similarity search and clustering of dense vectors. The hdbscan Clustering Library — hdbscan 0.8.1 documentation GitHub - eliorc/node2vec: Implementation of the node2vec algorithm. Zanimivosti iz tehnološkega sveta pošiljava tudi v elektronske nabiralnike. Naročilnica na Odbito pismo je tukaj. Razpravi o odbitih temah se lahko pridružite na Twitterju. Dosegljiva sva tudi na naslovu: odbita@rtvslo.si.  Podkast Odbita do bita je brezplačno na voljo v vseh aplikacijah za podkaste. Naročite se in podkast ocenite. 

Spacy Dragon by Mr Dee Swiss House

Book Vs Movie Coal Miner's Daughter The Margos Head to Butcher Hollow to Talk About One of the Top Country Music Artists of All TimeLoretta Lynn is a multi-hyphen: singer, songwriter, musician, wife, mother, and author. The Margos were thrilled to discover that the 1980 movie Coal Miner's Daughter was based on her 1976 autobiography of the same name. As superfans--we needed to bring this episode to our followers--you are all welcome!Loretta began her life in Butcher Hollow, Kentucky and later became one of the most revered & successful artists in recorded music. She was the oldest of eight children and by the time she was just shy of 16--she married her life mate Oliver “Doolittle” Webb. The film stars an incandescent Sissy Spacek as Lynn, Tommy Lee Jones as “Doo” Lynn, and Levon Helm (of “The Band” fame) as her father, Ted Webb. Sure, you can argue that the movie follows the “music biopic” re the humble beginnings, the creation of the first hit song, drugs, loneliness, and the downside of fame. But dammit if this film isn't first-class entertainment features incredible live singing performances and a complicated love story. And the hair in this film? To die for! Spacy won an Academy Award for her performance and the film aired on cable TV in the United States repeatedly throughout the 1980ss which means there is an entire generation that knows this movie backward & forwards. Lynn just released her second book Me & Patsy: Kickin' Up Dust which is all about her friendship with the legendary Patsy Cline. At 87--she continues to be creative and vital. An example of resiliency to us all!So between the autobiography and the movie, which did we like better? Click on the link below to find out!In this ep the Margos discuss:The life story of Loretta Lynn How Sissy Spacek got into character for the part of her lifetimeBehind the scenes tidbits from the setThe cast Sissy Spacey (Loretta Lynn,) Tommy Lee Jones (Doo Lynn,) Levon Helm (Ted Webb,) Phyllis Boyens-Liptak (Clary Webb,) William Sanderson (Lee Dollarhide,) and Beverly D'Angelo (Patsy Cline.)  Clips Featured:Coal Miner's Daughter  trailerDoo walks Loretta homeDoo and the “studio engineer”Beverly D'Angelo as Patsy ClineDoolittle in the studioOutro music Coal Miner's Daughter (Performed by Loretta Lynn)Join our Patreon page to help support the show! https://www.patreon.com/bookversusmovie Book Vs. Movie podcast https://www.facebook.com/bookversusmovie/Twitter @bookversusmovie www.bookversusmovie.comEmail us at bookversusmoviepodcast@gmail.comBrought to you by Audible.com You can sign up for a FREE 30-day trial here http://www.audible.com/?source_code=PDTGBPD060314004RMargo D. @BrooklynFitChik www.brooklynfitchick.com brooklynfitchick@gmail.comMargo P. @ShesNachoMama https://coloniabook.weebly.com/ 

Book Vs Movie Coal Miner's Daughter The Margos Head to Butcher Hollow to Talk About One of the Top Country Music Artists of All TimeLoretta Lynn is a multi-hyphen: singer, songwriter, musician, wife, mother, and author. The Margos were thrilled to discover that the 1980 movie Coal Miner's Daughter was based on her 1976 autobiography of the same name. As superfans--we needed to bring this episode to our followers--you are all welcome!Loretta began her life in Butcher Hollow, Kentucky and later became one of the most revered & successful artists in recorded music. She was the oldest of eight children and by the time she was just shy of 16--she married her life mate Oliver “Doolittle” Webb. The film stars an incandescent Sissy Spacek as Lynn, Tommy Lee Jones as “Doo” Lynn, and Levon Helm (of “The Band” fame) as her father, Ted Webb. Sure, you can argue that the movie follows the “music biopic” re the humble beginnings, the creation of the first hit song, drugs, loneliness, and the downside of fame. But dammit if this film isn't first-class entertainment features incredible live singing performances and a complicated love story. And the hair in this film? To die for! Spacy won an Academy Award for her performance and the film aired on cable TV in the United States repeatedly throughout the 1980ss which means there is an entire generation that knows this movie backward & forwards. Lynn just released her second book Me & Patsy: Kickin' Up Dust which is all about her friendship with the legendary Patsy Cline. At 87--she continues to be creative and vital. An example of resiliency to us all!So between the autobiography and the movie, which did we like better? Click on the link below to find out!In this ep the Margos discuss:The life story of Loretta Lynn How Sissy Spacek got into character for the part of her lifetimeBehind the scenes tidbits from the setThe cast Sissy Spacey (Loretta Lynn,) Tommy Lee Jones (Doo Lynn,) Levon Helm (Ted Webb,) Phyllis Boyens-Liptak (Clary Webb,) William Sanderson (Lee Dollarhide,) and Beverly D'Angelo (Patsy Cline.)  Clips Featured:Coal Miner's Daughter  trailerDoo walks Loretta homeDoo and the “studio engineer”Beverly D'Angelo as Patsy ClineDoolittle in the studioOutro music Coal Miner's Daughter (Performed by Loretta Lynn)Join our Patreon page to help support the show! https://www.patreon.com/bookversusmovie Book Vs. Movie podcast https://www.facebook.com/bookversusmovie/Twitter @bookversusmovie www.bookversusmovie.comEmail us at bookversusmoviepodcast@gmail.comBrought to you by Audible.com You can sign up for a FREE 30-day trial here http://www.audible.com/?source_code=PDTGBPD060314004RMargo D. @BrooklynFitChik www.brooklynfitchick.com brooklynfitchick@gmail.comMargo P. @ShesNachoMama https://coloniabook.weebly.com/ 

Digital Ad Market Update: 7/16 - 7/29 2022Joe, Kiersten, and Producer Alex are in the studio this week to break down everything business, tech, and culture related in 37 minutes or less. Tune in to hear it all!Top Stories:Facebook looks to copy TikTok's strategy for success, but will it work? Let's discuss.NASA clothing and accessories have been everywhere lately, and you may be wondering why. We have the answer. What's next for NASA swag?TikTok spends less for more viewership than Netflix does. What's next for this app that is growing super fast? Let's talk about it.Nuggets are showing up on every fast food chain's menu, and we have some thoughts about why. What nugget is next? We have a theory.Articles Mentioned:Facebook Copies TikTok: https://wapo.st/3oFQYdn NASA Swag Everywhere: https://cnn.it/3S7DeFG Chicken Sandwich War Meaning: https://bit.ly/3vnZaTi Popeyes Nuggets: https://bit.ly/3PKlVJ8 KFC Nuggets: https://cnn.it/3S4tA6VBoston Market Nuggets: https://prn.to/3S5Kime

The big news in Australia is the recently completed Plenary Council, and Lindsay, Caroline, and Lino begin their discussion of its results (more to come!), plus they have to talk about the new James Webb space telescope images! The post Keep it Spacy appeared first on StarQuest Media.

In this episode we speak to Ines Montani, co-founder and CEO of Explosion, a developer of Artificial Intelligence and Natural Language Processing technologies. We discuss how ML and NLP work behind the scenes, how developers should think about applied NLP, the common languages and frameworks used to build ML and NLP applications, and the challenges that come with running them at scale. About Ines MontaniInes Montani is co-founder and CEO of Explosion. A software developer working on Artificial Intelligence and Natural Language Processing technologies, her company Explosion are makers of spaCy, one of the leading open-source libraries for Natural Language Processing in Python, and Prodigy, a modern annotation tool for creating training data for machine learning models. In 2020, Montani became a Fellow of the Python Software Foundation.Other things mentioned:ExplosionspaCyProdigyTensorflowHugging Face PythonCypressHyperSlackLet us know what you think on Twitter:https://twitter.com/consoledotdevhttps://twitter.com/davidmyttonhttps://twitter.com/_inesmontani Or by email: hello@console.devAbout ConsoleConsole is the place developers go to find the best tools. Our weekly newsletter picks out the most interesting tools and new releases. We keep track of everything - dev tools, devops, cloud, and APIs - so you don't have to. Sign up for free at: https://console.devRecorded: 2022-04-06

Summary Machine learning is a force multiplier that can generate an outsized impact on your organization. Unfortunately, if you are feeding your ML model garbage data, then you will get orders of magnitude more garbage out of it. The team behind Galileo experienced that pain for themselves and have set out to make data management and cleaning for machine learning a first class concern in your workflow. In this episode Vikram Chatterji shares the story of how Galileo got started and how you can use their platform to fix your ML data so that you can get back to the fun parts. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out! Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you. Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started! Your host is Tobias Macey and today I’m interviewing Vikram Chatterji about Galileo, a platform for uncovering and addressing data problems to improve your model quality Interview Introduction How did you get involved in machine learning? Can you describe what Galileo is and the story behind it? Who are the target users of the platform and what are the tools/workflows that you are replacing? How does that focus inform and influence the design and prioritization of features in the platform? What are some of the real-world impacts that you have experienced as a result of the kinds of data problems that you are addressing with Galileo? Can you describe how the Galileo product is implemented? What are some of the assumptions that you had formed from your own experiences that have been challenged as you worked with early design partners? The toolchains and model architectures of any given team is unlikely to be a perfect match across departments or organizations. What are the core principles/concepts that you have hooked into in order to provide the broadest compatibility? What are the model types/frameworks/etc. that you have had to forego support for in the early versions of your product? Can you describe the workflow for someone building a machine learning model and how Galileo fits across the various stages of that cycle? What are some of the biggest difficulties posed by the non-linear nature of the experimentation cycle in model development? What are some of the ways that you work to quantify the impact of your tool on the productivity and profit contributions of an ML team/organization? What are the most interesting, innovative, or unexpected ways that you have seen Galileo used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Galileo? When is Galileo the wrong choice? What do you have planned for the future of Galileo? Contact Info LinkedIn Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links Galileo F1 Score Tensorflow Keras SpaCy Podcast.__init__ Episode Pytorch Podcast.__init__ Episode MXNet Jax The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

Wywiad z dr Inez Okulską z Państwowego Instytutu Badawczego NASK.Rozmawiamy m.in:- o tym, jaka była rola NASKu we wprowadzaniu Internetu do Polski i jak kontynuuje to dzieło dziś,- o działalności naukowej prowadzonej przez NASK *(zwanej dalej "Małym Stanford")- o tym, czym zajmuje się Zakład Inżynierii Lingwistycznej i Analizy Tekstu kierowany przez Inez- o wspomaganiu moderatorów serwisu Wykop.pl- o obciążeniu psychicznym osób monitorujących treści w Internecie- o tym, czym jest model StyloMetrix i o rodzajach metryk, które można do niego samodzielnie dodawać- o polskiej i angielskiej wersji SpaCy- o tym, czy zawsze wielkie modele transformerowe są lepsze od tych mniejszych, opartych o wiedzę lingwistyczną- o klasyfikacji tekstów bez zaglądania w ich treść- a także o kluczowej roli styku biznes-nauka, bez której obie strony tracą wiele możliwościPrzydatne linki:StyloMetrix:kod: https://github.com/ZILiAT-NASK/StyloMetrixartykuł: https://wydawnictwo.umg.edu.pl/pp-rai2022/pdfs/41_pp-rai-2022-121.pdfProfil Inez na LN: https://www.linkedin.com/in/inezokulska/Książka o podcastowaniu - "Jak zacząć podcast" Michał Dulemba - poradnik podcastera  Posłuchaj więcej odcinków na:nieliniowy.pl - podcast na temat sztucznej inteligencjiNapisz do mnie:Michal Dulemba | LinkedInSubskrybuj podcast:Apple PodcastsSpotifyGoogle PodcastsPodcast AddictRSSKorzystam z: Buzzsprout (hosting odcinków):https://www.buzzsprout.com/?referrer_id=1783532Riverside (aplikacja do zdalnego nagrywania):https://www.riverside.fm/?via=dulemba

Nlp --- Send in a voice message: https://anchor.fm/david-nishimoto/message

Learn the Mind-Body, Medical Intuitive Solutions to unearthing the intuitive ability behind brain fog, and distractibility and also successfully remembering and succeeding in the rational practical world.  This podcast features audience participation. Would you like to get a reading with Dr. Mona Lisa or just be a part of the podcast taping? Follow Dr. Mona Lisa on Facebook to find out when the podcasts tapings are happening and grab the Zoom link. Follow Dr. Mona Lisa on Instagram @drmonalisa1 to catch the Instagram live broadcasts. Can't get through during the tapings? Consider a private medical intuitive reading with Dr. Mona Lisa. Go here for more information ***Readings are educational only and are not intended to provide a physician-patient relationship, give diagnoses, prescribe treatment or do psychotherapy. Please contact your health care provider to obtain treatment. If you are in an emergency, please go to your nearest emergency room. Learn more about your ad choices. Visit megaphone.fm/adchoices

Watch the live stream: Watch on YouTube About the show Sponsored by FusionAuth: pythonbytes.fm/fusionauth Special guest: Ian Hellen Brian #1: gensim.parsing.preprocessing Problem I'm working on Turn a blog title into a possible url example: “Twisted and Testing Event Driven / Asynchronous Applications - Glyph” would like, perhaps: “twisted-testing-event-driven-asynchrounous-applications” Sub-problem: remove stop words ← this is the hard part I started with an article called Removing Stop Words from Strings in Python It covered how to do this with NLTK, Gensim, and SpaCy I was most successful with remove_stopwords() from Gensim from gensim.parsing.preprocessing import remove_stopwords It's part of a gensim.parsing.preprocessing package I wonder what's all in there? a treasure trove gensim.parsing.preprocessing.preprocess_string is one this function applies filters to a string, with the defaults almost being just what I want: strip_tags() strip_punctuation() strip_multiple_whitespaces() strip_numeric() remove_stopwords() strip_short() stem_text() ← I think I want everything except this this one turns “Twisted” into “Twist”, not good. There's lots of other text processing goodies in there also. Oh, yeah, and Gensim is also cool. topic modeling for training semantic NLP models So, I think I found a really big hammer for my little problem. But I'm good with that Michael #2: DevDocs via Loic Thomson Gather and search a bunch of technology docs together at once For example: Python + Flask + JavaScript + Vue + CSS Has an offline mode for laptops / tablets Installs as a PWA (sadly not on Firefox) Ian #3: MSTICPy MSTICPy is toolset for CyberSecurity investigations and hunting in Jupyter notebooks. What is CyberSec hunting/investigating? - responding to security alerts and threat intelligence reports, trawling through security logs from cloud services and hosts to determine if it's a real threat or not. Why Jupyter notebooks? SOC (Security Ops Center) tools can be excellent but all have limitations You can get data from anywhere Use custom analysis and visualizations Control the workflow…. workflow is repeatable Open source pkg - created originally to support MS Sentinel Notebooks but now supports lots of providers. When I start this 3+ yrs ago I thought a lot this would be in PyPI - but no

Und schon kommen wir dem Ende näher: Die 9. Folge von »Abgespaced« führt uns zu den Buchstaben Y wie Yucatan und Z wie Zwergplanet und somit zur letzten Folge unseres Weltraum ABC. Das Y hat selbst unsere Universalintelligenz Spacy herausgefordert. Doch wir haben etwas gefunden, das Euch astronomisch die Füße unter dem Boden wegzieht: Es geht um riesengroße Einschlagskrater, das Ende der Dinosaurier und Asteroiden, die unserer Erde gefährlich werden könnten. Unser Kinderreporter Milo war zu Besuch im Naturkundemuseum und hat sich von Daniela Schwarzer alles über den Yucatan-Krater, den Einschlag des Asteroiden und das Ende der Dino-Zeit erklären lassen. Doch wie gefährlich können Asteroiden noch heute für uns werden? Das haben wir für Euch im Deutschen Zentrum für Luft-und Raumfahrt nachgefragt. Hier arbeitet Christian Gritzner, der ganz genau weiß, worauf es ankommt, wenn ein Asteroid zu nah an der Erde vorbeisaust. Und so landen wir nicht nur bei Z wie zu Ende, sondern auch bei Z wie Zwergplanet. Neben den 8 Planeten unseres Sonnensystems gab es bis 2006 nämlich noch einen neunten Planeten: Pluto. Heute kennen wir ihn »nur« noch als Zwergplanet und das obwohl er gar kein Zwerg ist. Doch warum ist Pluto heute kein Planet mehr? Welche Voraussetzungen braucht es für einen Himmelskörper ein Planet zu sein? Alle kuriosen Fakten rund um Pluto liefern euch Spacy und Kristin in dieser allerletzten Folge. Aber pssst: Staffel zwei folgt! Bilder und Videos von Pluto durch die Raumsonde New Horizons: https://www.nasa.gov/feature/new-horizons-top-10-pluto-pics https://www.nasa.gov/mission_pages/newhorizons/videos/index.html Mit dabei sind unsere Kinderreporter Milo und Jonathan, Moderatorin Kristin Linde sowie die Interviewpartner*innen: Daniela Schwarz vom Naturkundemuseum und Christian Gritzner vom Deutschen Zentrum für Luft-und Raumfahrt.

Die 8. Folge von »Abgespaced« führt uns zu den Buchstaben V wie Voyager, W wie Weltraumschrott und X wie Xtreme im All. Unser Kinderreporter Milo hat sich dieses Mal die Frage gestellt: Können wir mit Außerirdischen kommunizieren? Und wenn ja, wie machen wir das? Was für eine Nachricht schickt man am besten an außerirdische Wesen im All? Diese Fragen führen uns zu den beiden NASA-Raumsonden Voyager 1 und 2. Seit 40 Jahren senden sie Signale vom Rand des Sonnensystems. Mit an Bord ist eine goldene Schallplatte mit allerhand versteckten Nachrichten an mögliche Außerirdische. Was es damit auf sich hat und woran man alles denken muss, wenn man Kontakt zu fernen Planeten aufnehmen will, erklärt uns Stefan Gotthold von der Stiftung Planetarium Berlin. Weiter geht's mit W wie Weltraumschrott: Das Weltall als Mülldeponie? Mittlerweile kreist so viel Weltraumschrott um unsere Erde, dass das sogar eine Gefahr für die Internationale Raumstation ISS darstellt. Doch wie viel Müll befindet sich eigentlich im Weltraum? Kann dieser uns gefährlich werden? Und welche Möglichkeiten gibt es, das Weltall nicht in eine Mülldeponie zu verwandeln? All diese Fragen beantwortet uns Johannes Herzog vom Deutschen Zentrum für Luft-und Raumfahrt in Oberpfaffenhofen. Zu guter Letzt landet unser Weltraum-ABC bei X wie Xtreme: Habt ihr schon mal von Planeten mit heißem, gefrorenen Eis gehört? Oder hexenartige Nebel mit Hakennase? Dann vielleicht vom größten jemals entdeckten Stern im All? Nein? Dann nehmen euch Kristin und Spacy bei ihrem letzten Zwischenstopp mit auf einen Streifzug durch die extremsten Orte im Weltall – vom größten jemals entdeckten Stern bis hin zum kältesten Ort im Universum. Mit dabei sind wieder viele Kinderreporter*innen, Moderatorin Kristin Linde und die Interviewpartner Stefan Gotthold, Leitung Bildung, Stiftung Planetarium Berlin und Johannes Herzog, Deutsches Zentrum für Luft-und Raumfahrt.

MLOps Coffee Sessions #83 with Vincent Warmerdam, Better Use cases for Text Embeddings. // Abstract Text embeddings are very popular, but there are plenty of reasons to be concerned about their applications. There's algorithmic fairness, compute requirements as well as issues with datasets that they're typically trained on. In this session, Vincent gives an overview of some of these properties while also talking about an underappreciated use-case for the embeddings: labeling! // Bio Vincent D. Warmerdam is a senior data professional who worked as an engineer, researcher, team lead, and educator in the past. He's especially interested in understanding algorithmic systems so that one may prevent failure. As such, he has a preference for simpler solutions that scale, as opposed to the latest and greatest from the hype cycle. He currently works as a Research Advocate at Rasa where he collaborates with the research team to explain and understand conversational systems better. Outside of Rasa, Vincent is also well known for his open-source projects (scikit-lego, human-learn, doubtlab, and more), collaborations with open source projects like spaCy, his blog over at koaning.io, and his calm code educational project. --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletter and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Skylar on LinkedIn: https://www.linkedin.com/in/skylar-payne-766a1988/ Connect with Vincent on LinkedIn: https://www.linkedin.com/in/vincentwarmerdam/

Our guest this week is Ines Montani, co-founder and CEO of Explosion, a company based out of Berlin that produce tools that you probably know and love like Spacy, a Python Natural Language Processing library and Prodigy, a data annotation tool. I've always found Ines to be personally inspiring in the work that she and her team produce as well as how they present themselves to the world, so it was a real pleasure to get to dive into the weeds as to exactly how that happens. We also discuss how NLP works in production, what reproducibility means for ML projects and much more. Special Guest: Ines Montani.

In this episode, we have a whole house. Not only will we have the whole crew we have our close Rhoan joining us, but also we have a special guest tonight in Luis Aliaga ‘ Lou Bernard'. Live at 10 pm EST on Facebook, YouTube, and most of your favorite podcast streaming platforms! https://www.youtube.com/hashtag/cigars (#cigars) https://www.youtube.com/hashtag/cigar (#cigar) https://www.youtube.com/hashtag/cigarlife (#cigarlife) https://www.youtube.com/hashtag/cigaraficionado (#cigaraficionado) https://www.youtube.com/hashtag/botl (#botl) https://www.youtube.com/hashtag/cigarporn (#cigarporn) https://www.youtube.com/hashtag/cigarsociety (#cigarsociety) https://www.youtube.com/hashtag/cigaroftheday (#cigaroftheday) https://www.youtube.com/hashtag/sotl (#sotl) https://www.youtube.com/hashtag/cigarlover (#cigarlover) https://www.youtube.com/hashtag/cigarsmoker (#cigarsmoker) https://www.youtube.com/hashtag/cigarlifestyle (#cigarlifestyle) https://www.youtube.com/hashtag/cigarsnob (#cigarsnob) https://www.youtube.com/hashtag/cigarworld (#cigarworld) https://www.youtube.com/hashtag/cigarsofinstagram (#cigarsofinstagram) https://www.youtube.com/hashtag/cigarphotography (#cigarphotography) https://www.youtube.com/hashtag/habanos (#habanos) https://www.youtube.com/hashtag/smoke (#smoke) https://www.youtube.com/hashtag/cigarboss (#cigarboss) https://www.youtube.com/hashtag/cigarculture (#cigarculture) https://www.youtube.com/hashtag/cigarians (#cigarians) https://www.youtube.com/hashtag/tobacco (#tobacco) https://www.youtube.com/hashtag/cigarlounge (#cigarlounge) https://www.youtube.com/hashtag/nowsmoking (#nowsmoking) https://www.youtube.com/hashtag/smoking (#smoking) https://www.youtube.com/hashtag/cigarsmoking (#cigarsmoking) https://www.youtube.com/hashtag/cigarlovers (#cigarlovers) https://www.youtube.com/hashtag/cigartime (#cigartime) https://www.youtube.com/hashtag/cigarstyle (#cigarstyle) https://www.youtube.com/hashtag/whiskey (#whiskey) https://www.youtube.com/hashtag/cohiba (#cohiba) https://www.youtube.com/hashtag/cigarsocialclub (#cigarsocialclub) https://www.youtube.com/hashtag/cigarsdaily (#cigarsdaily) https://www.youtube.com/hashtag/stogie (#stogie) https://www.youtube.com/hashtag/cigarlove (#cigarlove) https://www.youtube.com/hashtag/cigarstagram (#cigarstagram) https://www.youtube.com/hashtag/cuba (#cuba) https://www.youtube.com/hashtag/cubancigars (#cubancigars) https://www.youtube.com/hashtag/luxury (#luxury) https://www.youtube.com/hashtag/cigaraficionados (#cigaraficionados) https://www.youtube.com/hashtag/pssita (#pssita) https://www.youtube.com/hashtag/bourbon (#bourbon) https://www.youtube.com/hashtag/whisky (#whisky) https://www.youtube.com/hashtag/cigarsmokers (#cigarsmokers) https://www.youtube.com/hashtag/cigarsandwhiskey (#cigarsandwhiskey) https://www.youtube.com/hashtag/humidor (#humidor) https://www.youtube.com/hashtag/cigarfamily (#cigarfamily) https://www.youtube.com/hashtag/cubancigar (#cubancigar) https://www.youtube.com/hashtag/montecristo (#montecristo) https://www.youtube.com/hashtag/cigarcollector (#cigarcollector) https://www.youtube.com/hashtag/zigarren (#zigarren) https://www.youtube.com/hashtag/cigarclub (#cigarclub) https://www.youtube.com/hashtag/lifestyle (#lifestyle) https://www.youtube.com/hashtag/cigarsmoke (#cigarsmoke) https://www.youtube.com/hashtag/cigarro (#cigarro) https://www.youtube.com/hashtag/puros (#puros) https://www.youtube.com/hashtag/stogies (#stogies) https://www.youtube.com/hashtag/cuban (#cuban) https://www.youtube.com/hashtag/cigarian (#cigarian) 10pm EST on Facebook/Instagram/YouTube and all of your favorite podcast streaming services!!! https://www.youtube.com/hashtag/thedistrictofmisfitsshow (#thedistrictofmisfitsshow) https://www.youtube.com/hashtag/diorashleybrown (#diorashleybrown) https://www.youtube.com/hashtag/podcast (#podcast) https://www.youtube.com/hashtag/podcastersofinstagram (#podcastersofinstagram)...

Episode Summary:  Today, we speak with Bob Spacy of the family-owned Superior Pool Solutions, which he has operated alongside his wife Sheri since 2014. Superior offers full-service, repairs, tile cleaning, and remodeling.  Bob discusses his inspiration behind his “Truck Talks” series on Instagram and how it blew up in ways he never expected, including getting him seen as a mentor to pool guys everywhere.  “It was my way of trying to communicate with other pool guys that we're all in this together,” says Bob. “And it just kind of grew from there.” In fact, the series ultimately tripled business for the Spacy family.  Bob then goes into his foundation, Scary Christmas House, talking about how a fateful trip to Disneyland led to a creative endeavor that brought his blended family closer than ever before and grew into a philanthropic initiative that gained steam nationwide.  Topics Discussed:  01:16 - An introduction to Bob Spacy Jr.  02:25 - How “Truck Talk” series tripled his business 05:37 - Giving value as a business owner  07:05 - Why Bob began the Scary Christmas House  10:45 - Being featured on national television  12:41 - How the house turned into a foundation  15:11 - How Bob uses the money from the foundation  17:49 - Bob's new goals for the foundation  20:32 - How to donate to the Scary Christmas Foundation      Sponsors:  Skimmer  Connect with Guest:  Scary Christmas House Website  Scary Christmas House Instagram  Scary Christmas House Facebook  Scary Christmas House TikTok  Scary Christmas House Fox 10 Feature  Superior Pool Solutions Website  Superior Pool Solutions Instagram  Superior Pool Solutions Facebook    Connect with Pool Chasers:  Website  Instagram  Facebook  Facebook Group  Twitter  YouTube  Patreon      Key Quotes from Episode:  [With Truck Talks,] I wanted the other pool guys out there to relate to me and to know that, “You guys aren't alone.” I'm dealing with the same things every day that you are.  TikTok is a whole ‘nother beast that I haven't even gotten into. I know some guys out there that are just slaying it on TikTok. If you use the media platform properly and you just stay humble with it and put your nose down and grind, it's amazing what it will do for you. 

Neste episódio conversamos com Diogo Cortiz, Cientista Cognitivo que trabalha na interseção de Pesquisa em Inteligência Artificial, Design e Mídia, com experiência em atuação como Gerente de Produto de IA (Criação de Conjunto de Dados, Anotação de Dados, Treinamento de Modelo, Validação de Modelo, Medida de Impacto, Equidade e Métricas Éticas) PNL e NLU (modelos Transformers, Spacy, Hugging Face, PyTorch). Suas pesquisas estão focadas na compreensão de tecnologias emergentes e seus impactos em diferentes sociedades ao redor do mundo.   Falamos sobre o que a IA tem a ver com a ciência cognitiva, com a web imersiva e o metaverso, os dilemas éticos, o design afetivo, o futurismo e muito mais!  Acesse nosso post para ter acesso a links e referências: https://medium.com/lets-data/

--

On the final episode of the debut season of AGGRESSIVE KINDNESS, I will share with you my greatest invention yet. I call it BELIEF BANK, and it has been the single most effective tool in my journey towards mental health, inner peace, and creative impact. It's literally an imaginary bank I designed and installed in myself where I deposit all my gifts, talents, and powers. My BELIEF BANK turns these deposits into the most valuable currency in the universe : SELF BELIEF. The goal is to maintain 100% self belief minimum within yourself at all times. At 100% you have all you need to accomplish anything without needing others to define your worth, potential, or trajectory. For anyone whose magical talents and confidence are diminished by their reliance on others for approval, validation or support. LISTEN, BUILD, INSTALL, and AGREE to keep yourself at 100% minimum. Invest in yourself with BELIEF BANK.

This episode of AGGRESSIVE KINDNESS is equal parts inspirational manifesto and instruction manual. THE PATH OF PASSION is the act of building a life around your magical talents and undeniable passions to achieve your intended maximum impact. It's the hardcore decision to take control of the journey and fearlessly build your own world using your natural gifts, unique ideas, and self belief as the tools. THE PATH OF PASSION is the end of compromising your talents and passions for anyone else's dream but your own. I will walk you through the 13 phases of this wild and wonderful creative adventure, from deciding to just fucking start all the way through creation, success, reflection, and rebirth. This is a destiny check for absolutely anyone who feels that seed of unrealized greatness inside. There is a more passionate path. attn: current and future artists, creators, writers, thinkers, dreamers, performers etc...

Omar Sanseviero - Machine Learning Engineer en HuggingFace. https://huggingface.co/ https://twitter.com/osanseviero Temas clave: Cómo HuggingFace y Spacy colaboraron para crecer el ecosistema open source NLP. No existe una ruta de oro para entrar a aprender IA. Busca resolver un problema y a partir de ahí encuentra las herramientas para resolverlo. Tip: PyTorch para comenzar. Haz proyectos interesantes para ti. El material está gratis en internet. Conocimientos necesarios para hacer Machine Learning de frontera. Sin tantas matemáticas harás maravillas. Crea tu portafolio y gana exposición en el HUB de HuggingFace. Sube modelos y demos al HUB con solo Python. Streamlit como frontend/backend para tus aplicaciones. Luego súbelo a HuggingFace. Todo gratis. Tendencia: Machine learning aplicado a nuevas áreas. Aplicar las nuevas técnicas a aplicaciones como 3D, audio, química, etc. Transformers: en el centro de la innovación. Están mostrando alta aplicabilidad a otras áreas como imágenes y audio. Cómo un latinoamericano puede llegar a una empresa que esté avanzando la frontera del machine learning. Aplica sin miedo a trabajos, aprende inglés, comparte con la comunidad (Twitter, Discord), crea contenido, obtén visibilidad. Actitud de crecimiento. ¿Es necesaria la licenciatura/maestría en computación para hacer machine learning? El síndrome del impostor. Todos lo hemos tenido. Producido por ELIA - Escuela Latinoamericana de Inteligencia Artificial: ELIA (@elia_latam) Omar Espejel (@espejelomar) Estefanía Arias (@yharyarias5) Yeder Laura (@yederlvicente)

How is it July already? In this episode our local Bookdragon SandyJ has not only some routine suggestions for you, but also two very different book recommendations. While the non-fiction recommendation „Lost Connections“ by Johann Hari explores the difficult topic of depression and the human society‘s condition. The Science Fiction recommendation is, while it explores equally complicated topics, a very cozy and enjoyable read: „The Long Way to a Small, Angry Planet“ by Becky Chambers. A Heroine's Journey through space? Yes, please. Stream on Sunday, July 4th, 7 PM local time. Drops on Monday, July 5th, noon-ish, Oldschool Radio Broadcast Tuesday July 13th, 10 AM, 104.8 UKW Bookdragon, Producer: SandyJ Music: Erwin Schmidt Book Recommendations Johann Hari „Lost Connections – Uncovering the Real Causes of Depression – and the unexpected Solutions“ (2018) https://www.goodreads.com/book/show/34921573-lost-connections Becky Chambers „The Long Way to a Small, Angry Planet“ (2014) https://www.goodreads.com/book/show/22733729-the-long-way-to-a-small-angry-planet Book Releases Elizabeth Lim „Six Crimson Cranes“ (July 6th) https://www.goodreads.com/book/show/53457092-six-crimson-cranes T.J. Klune „Flash Fire“ (July 13th) https://www.goodreads.com/book/show/55169010-flash-fire Lish McBride „Curses“ (July 20th) https://www.goodreads.com/book/show/43207851-curses Other Stuff Mentioned Legs up the wall pose – Yoga with Adriene Video https://www.youtube.com/watch?v=_OQEIiZLY-0 How to be „That Girl“ – Rowena Tsai Video https://www.youtube.com/watch?v=wvMSSHO6_wo Fairyloot May Box Unboxing – Brandie Shanae https://www.youtube.com/watch?v=JX15LR9BYCg

In this week's episode, the Krewe takes a trip down memory lane to reminisce about what many consider to be the “Golden Age” of Japanese animation, 1990-2010. Nigel, Jennifer, & Doug talk some prolific titles like Dragon Ball Z, Sailor Moon, Naruto, & One Piece, while paying respects to some non-mainstream (but popular) shows. Packed to the brim with loves, hates, and hot takes, they take you on a fun audio journey exploring shows they loved growing up and how the ingestion and perception of anime changed with the world around it.Be on the lookout for part 3 later this month!For more on the Japan Society of New Orleans:http://www.japansocietyofneworleans.org/

Vor etwa einem Monat bekamen wir die Anfrage von einer Hörerin (im Gespräch fälschlicherweise als Hörer bezeichnet, sorry), doch mal etwas ausführlicher über unsere Erfahrungen mit NLP, Transformern und spaCy zu sprechen. Da wir selbst noch gar nicht so viele Erfahrungen mit spaCy gesammelt haben, haben wir Ines (Mitgründerin von Explosion und eine der Hauptentwicklerinnen von spaCy) gefragt, ob sie sich mit uns darüber unterhalten möchte. Wir hatten auch noch nie einen Expertengast, der räumlich derart weit entfernt war (Australien) und daher etwas Bammel wegen Internet/Audio, aber ging gut. Für euch getestet: Teilnahme an diesem Podcast möglich, solange ihr irgendwo auf diesem Planeten seid :).     Shownotes Unsere E-Mail für Fragen, Anregungen & Kommentare: hallo@python-podcast.de Was ist NLP? Ines / Explosion Natural Language Processing (NLP) Document Classification (Text Classification / Categorization) Attention Is All You Need - Paper, das hinter den aktuellen Fortschritten mit Transformern steckt Self-supervised learning: The dark matter of intelligence - Blogpost von Yann LeCun und Ishan Misra Language Model Domain-driven Design spaCy prodigy spaCy Lineare Modelle Software 2.0 Building the Software 2 0 Stack (Andrej Karpathy) prodigy gebaut mit FastAPI / React Advanced NLP with spaCy (Online-Kurs über spaCy von Ines) Sich Gedanken machen - oft unterschätzt! Blackstone - eine spaCy Pipeline zur Analyse von juristischen Texten Open Source Halloween Papers Öffentliches Tag auf konektom

Mit Christian haben wir uns heute mal wieder ein bisschen mehr über Machine Learning etc. unterhalten. Was wäre, wenn man Jupyter-Notebooks als IDE verwenden wollte (nbdev)? Was braucht man eigentlich heutzutage so an Hardware, wenn man Modelle trainieren will? Ausserdem haben wir ein bisschen auf der Mikrofon/Headset-Seite aufgerüstet (keine Ahnung, ob man das hört).     Shownotes Unsere E-Mail für Fragen, Anregungen & Kommentare: hallo@python-podcast.de News aus der Szene Numpy 1.20 Release Pandas 1.2 Release Spacy v3 Release Ben Gorman: Python NumPy For Your Grandma, Python Pandas For Your Grandpa Mypy 0.800 Release Pip 21.0 Release appenv, batou NBDEV nbdev I don't like notebooks.- Joel Grus Literate Programming I Like Notebooks - Jeremy Howard google colab Binder Buch: Deep Learning for Coders with fastai and PyTorch Machine Learning Recap ocr: Tesseract Vektorrechner / Tensor Cores / TPUs Hardware: Which GPU(s) to Get for Deep Learning Criteo: Display Advertising Challenge Netflix Prize Öffentliches Tag auf konektom

In today's episode, senior technical evangelist Mady Mantha is joined by Ines Montani and Matthew Honnibal, co-founders of Explosion, a software company that specializes in developer tools for Natural Language Processing (NLP) applications, and core contributors to spaCy, an open-source software library for advanced NLP. Join in to learn about how to build resilient NLP applications, some challenges and pitfalls developers typically experience, and some ways to overcome them. --- Send in a voice message: https://anchor.fm/rasachats/message

Sofie and Ines walk us through how the new spaCy library helps build end to end SOTA natural language processing workflows. Ines Montani is the co-founder of Explosion AI, a digital studio specializing in tools for AI technology. She's a core developer of spaCy, one of the leading open-source libraries for Natural Language Processing in Python and Prodigy, a new data annotation tool powered by active learning. Before founding Explosion AI, she was a freelance front-end developer and strategist. https://twitter.com/_inesmontani Sofie Van Landeghem is a Natural Language Processing and Machine Learning engineer at Explosion.ai. She is a Software Engineer at heart, with an absurd love for quality assurance and testing, introducing proper levels of abstraction, and ensuring code robustness and modularity. She has more than 12 years of experience in Natural Language Processing and Machine Learning, including in the pharmaceutical industry and the food industry. https://twitter.com/oxykodit https://spacy.io/ https://prodi.gy/ https://thinc.ai/ https://explosion.ai/ Topics covered: 0:00 Sneak peek 0:35 intro 2:29 How spaCy was started 6:11 Business model, open source 9:55 What was spaCy designed to solve? 12:23 advances in NLP and modern practices in industry 17:19 what differentiates spaCy from a more research focused NLP library? 19:28 Multi-lingual/domain specific support 23:52 spaCy V3 configuration 28:16 Thoughts on Python, Syphon, other programming languages for ML 33:45 Making things clear and reproducible 37:30 prodigy and getting good training data 44:09 most underrated aspect of ML 51:00 hardest part of putting models into production Visit our podcasts homepage for transcripts and more episodes! www.wandb.com/podcast Get our podcast on Apple, Spotify, and Google! Apple Podcasts: bit.ly/2WdrUvI Spotify: bit.ly/2SqtadF Google:tiny.cc/GD_Google We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it! Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research: tiny.cc/wb-salon Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning: bit.ly/wb-slack Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices. app.wandb.ai/gallery

NLTK: swiss army knife. Gensim: LDA topic modeling, n-grams. spaCy: linguistics. transformers: high-level business NLP tasks.

The podcasts return with new content, especially about NLP: BERT, transformers, spaCy, Gensim, NLTK. Accompanied by a community project - Gnothi, a journal that uses AI to provide insights and resources. Website https://gnothiai.com, project https://github.com/lefnire/gnothi. Share the website on social media and email me a link/screenshot for free access to Machine Learning Applied for 3 months; contribute to the Github repository for free access for life.

We talked about how computers increased Ines's creativity as a teen. We talked about Ines's disappointment at German university studies. We discussed the steps she took away from computer sciences, and how she ended up combining two of her passions: developing software and linguistics into one new field: computer linguistics. We finished by talking about having developers as customers and dog-feeding your own products.Here are the links of the show:https://www.twitter.com/_inesmontanihttps://explosion.aihttps://spacy.iohttps://prodi.gyhttps://ines.iohttps://ines.io/blog/how-i-started-codingCreditsMusic Aye by Yung Kartz is licensed CC BY-NC-ND 4.0.Your host is Timothée (Tim) Bourguignon, more about him at timbourguignon.fr.Gift the podcast a rating on one of the major platforms https://devjourney.info/subscribe.htmlSupport the podcast, support us on Patreon: https://bit.ly/devjpatreonSupport the show (http://bit.ly/2yBfySB)

Episode II of Mayonaka Hour features the king of City Pop; Tatsuro Yamashita. We explore his methods, influences, and inspirations. We also review his first 6 studio albums Circus Town, Spacy, Come Along, Moonglow, Ride On Time, and For You.

Special guest: Ines Montani Michael #1: VS Code Device Simulator Want to experiment with MicroPython? Teaching a course with little IoT devices? Circuit Playground Express BBC micro:bit Adafruit CLUE with a screen Get a free VS code extension that adds a high fidelity simulator Easily create the starter code (main.py) Interact with all the sensors (buttons, motion sensors, acceleration detection, device shake detection, etc.) Deploy and debug on a real device when ready Had the team over on Talk Python. Brian #2: pytest 6.0.0rc1 New features You can put configuration in pyproject.toml Inline type annotations. Most user facing API and internal code. New flags - --no-header - --no-summary - --strict-config : error on unknown config key - --code-highlight : turn on/off code highlighting in terminal Recursive comparison for dataclass and attrs Tons of fixes Improved documentation There’s a list of breaking changes and deprications. But really, nothing in the list seems like a big deal to me. Plugin authors, including myself, should go test this. Already found one problem. pytest-check: stop on fail works fine, but failing tests marked with xfail show up as xpass. Gonna have to look into that. And might have to recruit Anthony to help out again. To try it: pip install pytest==6.0.0rc1 I’m currently running through the pytest book to make sure it all still works with pytest 6. So far, so good. The one hiccup I’ve found so far, TinyDB had a breaking change with 4.0, so you need to pip install tinydb==3.15.2 to get the tasks project to run right. I should have pinned that in the original setup.py. However, all of the pytest stuff is still valid. Guido just tweeted: “Yay type annotations in pytest!” Ines #3: TextAttack Python framework for adversarial attacks and data augmentation for natural language processing What are adversarial attacks? You might have seen examples like these: image classifier predicting a cat even if the image is complete noise people at protests wearing shirts and masks with certain patterns to trick facial recognition Google Translate hallucinating bible texts if you feed it nonsense or repetitive syllables What does it mean to "understand" a model? How does it behave in different situations, with unexpected data? We can't just inspect the weights – that's not how neural networks work To understand a model, we need to run it and find behaviours we don't like TextAttack lets you run various different “attacks” from the current academic literature It also lets you create more robust training data using data augmentation, for example, replacing words with synonyms, swapping characters, etc. Michael #4: What is the core of the Python programming language? By Brett Cannon, core developer Brett and I discussed Python implementation for WebAssembly before Get Python into the browser, but with the fact that both iOS and Android support running JavaScript as part of an app it would also get Python on to mobile. We have lived with CPython for so long that I suspect most of us simply think that "Python == CPython". PyPy tries to be so compatible that they will implement implementation details of CPython. Basically most implementations of Python strive to pass CPython's test suite and to be as compatible with CPython as possible. Python’s dynamic nature makes it hard to do outside of an interpreter That has led Brett to contemplate the question of what exactly is Python? How much would one have to implement to compile Python directly to WebAssembly and still be considered a Python implementation? Does Python need a REPL? Could you live without locals()? How much compatibility is necessary to be useful? The answer dictates how hard it is to implement Python and how compatible it would be with preexisting software. [Brett] has no answers It might make sense to develop a compiler that translates Python code directly to WebAssembly and sacrifice some compatibility for performance. It might make sense to develop an interpreter that targets WebAssembly's design but maintains a lot of compatibility with preexisting code. It might make sense to simply support RustPython in their WebAssembly endeavours. Maybe Pyodide will get us there. Michael’s thoughts: How about a Python standard language spec? A standard-library “standard???!?” spec. It’s possible - .NET did it. What would be build if we could build it with web assembly? Interesting options open up, say with NodeJS like capabilities, front-end frameworks This could be MUCH bigger if we got browser makes to support alternative runtimes through WebAssembly Brian #5: Getting started with Pathlib Chris May Blog post: Stop working so hard on paths. Get started with pathlib! PDF “field guide”: Getting started with Pathlib Really great introduction to Pathlib Some of the info This file as a path object: Path(__file__) Parent directory: Path(__file__).parent Absolute path: Path(__file__).parent.resolve() Two levels up: Path(__file__).resolve(strict=True).parents[1] See pdf for explanation. Current working dir: Path.cwd() Path building with / Working with files and folders Using glob Finding parts of paths and file names. Any time spent learning Pathlib is worth it. If I can do it in Pathlib, I do. It makes my code more readable. Ines #6: Data Version Control (DVC) We're currently working on v3.0 of spaCy and one of the big features is going to be a completely new way to train your custom models, manage end-to-end training workflows and make your experiments reproducible It will also integrate with a tool called DVC (short for Data Version Control), which we've started using internally DVC is an open-source tool for version control, specifically for machine learning and data Machine learning = code + data. You can check your code into a Git repo, but you can't really check in your datasets and model weights. So it's very difficult to keep track of changes. You can think of DVC as “Git for data” and the command line usage is actually pretty similar – for example, you run dvc init to initialize a repo and dvc add to start tracking assets DVC lets you track any assets by adding meta files to your repository. So everything, including your data, is versioned, and you can always go back to the commit with the best accuracy It also builds a dependency graph based on the inputs and outputs of each step, so you only have to re-run a step if things changed for example, you might have a preprocessing step that converts your data and then a step that trains your model. If the data hasn't changed, you don't have to re-run the preprocessing step. They recently released a new tool called CML (short for Continuous Machine Learning), which we haven't tried yet. CI for Machine Learning Previews look pretty cool: you can submit a PR with some changes and a GitHub action will run your experiment and auto-comment on the PR with the results, changes in accuracy and some graphs (similar to tools like Code Coverage etc.) Extra Michael: Podcast Python Search API package, by Anton Zhiyanov Mid-string f-string upgrades coming to PyCharm. And Flynt! via Colin Martin Ines: Built-in generic types in 3.9 (PEP 585): you can now write list[str] ! Brian: https://testandcode.com/120: FastAPI & Typer - Sebastián Ramírez Jokes Fast API Job Experience Sebastián Ramírez - @tiangolo I saw a job post the other day. It required 4+ years of experience in FastAPI. I couldn't apply as I only have 1.5+ years of experience since I created that thing. Maybe it's time to re-evaluate that "years of experience = skill level". Defragged Zebra

Working to create a search engine

Hello Groovers, Oh yeah, let's go back to the start of the Orinoco river for some smokin' and tokin' in the Liquid Sunshine discotheque with some deep raw funk. You know, the good stuff!! Good times, Deejay Maarten Vlot QC Liquid Sunshine is a weekly radio show on 2XX FM in Australia, and The Face Radio in Brooklyn, USA, playing the best Deep Funk, Rare Groove, Disco & Beats - All The Good Stuff. We also re-edit, DJ out in the wild, and produce mighty boss tunes. Catch Liquid Sunshine with Maarten Vlot as a podcast, browse the socials, or get in contact via this link: linktr.ee/liquidsunshineradio or Stream live at https://thefaceradio.com every Tuesday from 10 PM – Midnight EST / 3 – 5 AM GMT / 12 - 2 PM AEST or Stream live at www.2xxfm.org.au every Thursday from 9:30 - 10:30PM AEST / 10:30 - 11:30 AM GMT or Tune in on 98.3 FM in Canberra 9:30 - 10:30 pm, every Thursday

Hello Groovers,Oh yeah, let's go back to the start of the Orinoco river for some smokin' and tokin' in the Liquid Sunshine discotheque with some deep raw funk. You know, the good stuff!!Good times,Deejay Maarten Vlot QCLiquid Sunshine is a weekly radio show on 2XX FM in Australia, and The Face Radio in Brooklyn, USA, playing the best Deep Funk, Rare Groove, Disco & Beats - All The Good Stuff. We also re-edit, DJ out in the wild, and produce mighty boss tunes.Catch Liquid Sunshine with Maarten Vlot as a podcast, browse the socials, or get in contact via this link:linktr.ee/liquidsunshineradioorStream live at https://thefaceradio.comevery Tuesday from 10 PM – Midnight EST / 3 – 5 AM GMT / 12 - 2 PM AESTorStream live at www.2xxfm.org.auevery Thursday from 9:30 - 10:30PM AEST / 10:30 - 11:30 AM GMTorTune in on 98.3 FM in Canberra9:30 - 10:30 pm, every Thursday

Video Version: https://youtu.be/C5DGFSDlMBM Subscribe here to the newsletter: https://tinyletter.com/sanyambhutani In this episode, Sanyam Bhutani interviews a machine learning hero who's been inspiring and empowering many, many future heroes through their open source work: Ines Montani, co founder of explosion, the company behind spacy, prodigy and thinc.ai. They talk about all of the amazing work that is, and the team at explosion has been doing, her journey into the field of programming her journey into the field of NLP and her journey at explosion. They talk about all these things, all these three things along with all of the amazing work that Explosion has been putting out. Yes, Spacy, prodigy and thinc.ai, thinc,ai is the latest framework that has been put out along with open source development and the NLP industry. Personal Note: Yes, those are a lot of things that have been All (Luckily for us), been covered in this single podcast! This has been a treat for me & I hope you enjoy it as much as I did. Links: https://explosion.ai/about https://spacy.io https://prodi.gy https://thinc.ai Follow: Ines Montani: https://twitter.com/_inesmontani https://www.linkedin.com/in/inesmontani/ https://ines.io Sanyam Bhutani: https://twitter.com/bhutanisanyam1 Blog: sanyambhutani.com About: https://sanyambhutani.com/tag/chaitimedatascience/ A show for Interviews with Practitioners, Kagglers & Researchers and all things Data Science hosted by Sanyam Bhutani. You can expect weekly episodes every available as Video, Podcast, and blogposts. If you'd like to support the podcast: https://www.patreon.com/chaitimedatascience Intro track: Flow by LiQWYD https://soundcloud.com/liqwyd --- Send in a voice message: https://anchor.fm/chaitimedatascience/message

  Book Vs Movie  Coal Miner’s Daughter  The Margos Head to Butcher Hollow to Talk About One of the Top Country Music Artists of All Time Loretta Lynn is a multi-hyphen: singer, songwriter, musician, wife, mother, and author. The Margos were thrilled to discover that the 1980 movie Coal Miner’s Daughter was based on her 1976 autobiography of the same name. As superfans--we needed to bring this episode to our followers--you are all welcome! Loretta began her life in Butcher Hollow, Kentucky and later became one of the most revered & successful artists in recorded music. She was the oldest of eight children and by the time she was just shy of 16--she married her life mate Oliver “Doolittle” Webb. The film stars an incandescent Sissy Spacek as Lynn, Tommy Lee Jones as “Doo” Lynn, and Levon Helm (of “The Band” fame) as her father, Ted Webb.  Sure, you can argue that the movie follows the “music biopic” re the humble beginnings, the creation of the first hit song, drugs, loneliness, and the downside of fame. But dammit if this film isn’t first-class entertainment features incredible live singing performances and a complicated love story. And the hair in this film? To die for!  Spacy won an Academy Award for her performance and the film aired on cable TV in the United States repeatedly throughout the 1980ss which means there is an entire generation that knows this movie backward & forwards.  Lynn just released her second book Me & Patsy: Kickin’ Up Dust which is all about her friendship with the legendary Patsy Cline. At 87--she continues to be creative and vital. An example of resiliency to us all! So between the autobiography and the movie, which did we like better? Click on the link below to find out! In this ep the Margos discuss: The life story of Loretta Lynn  How Sissy Spacek got into character for the part of her lifetime Behind the scenes tidbits from the set The cast Sissy Spacey (Loretta Lynn,) Tommy Lee Jones (Doo Lynn,) Levon Helm (Ted Webb,) Phyllis Boyens-Liptak (Clary Webb,) William Sanderson (Lee Dollarhide,) and Beverly D’Angelo (Patsy Cline.)   Clips Featured: Coal Miner’s Daughter  trailer Doo walks Loretta home Doo and the “studio engineer” Beverly D’Angelo as Patsy Cline Doolittle in the studio Outro music Coal Miner’s Daughter (Performed by Loretta Lynn) Join our Patreon page to help support the show! https://www.patreon.com/bookversusmovie  Book Vs. Movie podcast https://www.facebook.com/bookversusmovie/ Twitter @bookversusmovie www.bookversusmovie.com Email us at bookversusmoviepodcast@gmail.com Brought to you by Audible.com You can sign up for a FREE 30-day trial here http://www.audible.com/?source_code=PDTGBPD060314004R Margo D. @BrooklynFitChik www.brooklynfitchick.com brooklynfitchick@gmail.com Margo P. @ShesNachoMama https://coloniabook.weebly.com/ 

The state of the art in natural language processing is a constantly moving target. With the rise of deep learning, previously cutting edge techniques have given way to robust language models. Through it all the team at Explosion AI have built a strong presence with the trifecta of SpaCy, Thinc, and Prodigy to support fast and flexible data labeling to feed deep learning models and performant and scalable text processing. In this episode founder and open source author Matthew Honnibal shares his experience growing a business around cutting edge open source libraries for the machine learning developent process.

Welcome to PyDataMCR Episode 12, This episode is all about Natural Language Processing, specifically Sentiment Analysis. Listen for our take on the approach to the complexity of text data, how we prepare the data, and the tools we can use to tease out sentiment. Sponsors LadBible - ladbible.com/ Cathcart Associates - cathcartassociates.com/ Horsefly Analytics - horseflyanalytics.com/ Resources Tutorials Natural Language Processing https://www.kaggle.com/learn/natural-language-processing Word embeddings for sentiment analysis https://towardsdatascience.com/word-embeddings-for-sentiment-analysis-65f42ea5d26e Sentiment Analysis in R https://www.kaggle.com/rtatman/tutorial-sentiment-analysis-in-r Tools NLTK - https://www.nltk.org/ SpaCy - https://spacy.io/ TextBlob - https://textblob.readthedocs.io/en/dev/ VADER - https://github.com/cjhutto/vaderSentiment Google Natural Language API - https://cloud.google.com/natural-language Azure Text Analytics API https://azure.microsoft.com/en-gb/services/cognitive-services/text-analytics/ Social Meetup - meetup.com/PyData-Manchester/ Slack - http://bit.ly/35KGOgR Twitter - @PyDataMCR

Sergio #1: Geocomputación con R Otro ejemplo de Bookdown para crear libros técnicos con R Rodo #2: D-Tale - Un cliente de Flask/React para visualizar estructuras de datos de Pandas. D-Tale combina Flask en back-end y React en front-end para brindarnos una manera fácil de ver y analizar las estructuras de datos de Pandas. Se integra a la perfección con las Jupyter Notebooks y las terminales Python/ IPython. Admite objetos Pandas como DataFrame, Series, MultiIndex, DatetimeIndex y RangeIndex. Sergio #3: El que trajo la app que te hace un mapa con todas las calles de una ciudad tiene otra app que hace Ridgeline plots de mapas Este parece ser un tema común aquí en QUAIL data jaja Sergio #4: ¿Qué es el tidyverse? por Rafa Gouveia - https://www.youtube.com/watch?v=uGg13_qOwhQ&list=PLbDLkhJ5sFvCWFbP4tAFALHkNWNFo_FiL 8 Herramientas Rodo #5: El breve resumen de la PyCon Colombia 2020 Increíbles keynote speakers como Andrew Godwin, Wes McKinney, Sarah Guido y Fernando Pérez, entre otros. Increíbles talleres con un track completo sobre Data Science, Web Development, IoT y otros. Repo de mi taller: https://github.com/RodolfoFerro/PyConCo20 Rodo #6: Thinc.ai - Una refrescante versión funcional del aprendizaje profundo, compatible con TUS bibliotecas FAVORITAS. Puedes cambiar entre frameworks. Realiza chequeo de tipos. Thinc nos permite describir árboles de objetos, con referencias nuestras propias funciones a través de archivos .cfg. Es súper ligero. Por los creadores de SpaCy y FastAPI... Extras: Sergio: Periodismo computacional - una clase de la universidad de Columbia - Un repositorio con los notebooks que estan usando en la clase este 2020 Ines montani repositorio base para crear cursos de python https://github.com/ines/course-starter-python y de R https://github.com/ines/course-starter-r Rodo: ¡Meetup de R para Data Science y Reinforcement Learning en Monterrey el próximo 18 de febrero! XII Congreso Mexicano de Inteligencia Artificial del 20 al 22 de mayo del 2020 en Ciudad Juárez, Chihuahua, México: http://smia.mx/comia/2020/ Gente bonita de Mérida y alrededores, asistan al Datostada: https://datostada.mx Meme de la semana: https://www.reddit.com/r/mathmemes/comments/f3eq3o/absolutely/ --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app --- Send in a voice message: https://anchor.fm/quaildata/message Support this podcast: https://anchor.fm/quaildata/support

For an out of this world Valentine, don’t head to the store. You might find just the card you’re looking for at NASA.

When a artist is involved in scandal what happen to all their past work. Is it now invalid? Well Kevin and Kurt are joined by Nicole Berastequi, and Ethan Norris to talk about the Cancel Culture!

Sponsored by Datadog: pythonbytes.fm/datadog Special guest: Vicki Boykis: @vboykis Michael #1: clize: Turn functions into command-line interfaces via Marcelo Follow up from Typer on episode 164. Features Create command-line interfaces by creating functions and passing them to [clize.run](https://clize.readthedocs.io/en/stable/api.html#clize.run). Enjoy a CLI automatically created from your functions’ parameters. Bring your users familiar --help messages generated from your docstrings. Reuse functionality across multiple commands using decorators. Extend Clize with new parameter behavior. I love how this is pure Python without its own API for the default case Vicki #2: How to cheat at Kaggle AI contests Kaggle is a platform, now owned by Google, that allows data scientists to find data sets, learn data science, and participate in competitions Many people participate in Kaggle competitions to sharpen their data science/modeling skills Recently, a competition that was related to analyzing pet shelter data resulted in a huge controversy Petfinder.my is a platform that helps people find pets to rescue in Malaysia from shelters. In 2019, they announced a collaboration with Kaggle to create a machine learning predictor algorithm of which pets (worldwide) were more likely to be adopted based on the metadata of the descriptions on the site. The total prize offered was $25,000 After several months, a contestant won. He was previously a Kaggle grandmaster, and won $10k. A volunteer, Benjamin Minixhofer, offered to put the algorithm in production, and when he did, he found that there was a huge discrepancy between first and second place Technical Aspects of the controversy: The data they gave asked the contestants to predict the speed at which a pet would be adopted, from 1-5, and included input features like type of animal, breed, coloration, whether the animal was vaccinated, and adoption fee The initial training set had 15k animals and the teams, after a couple months, were then given 4k animals that their algorithms had not seen before as a test of how accurate they were (common machine learning best practice). In a Jupyter notebook Kernel on Kaggle, Minixhofer explains how the winning team cheated First, they individually scraped Petfinder.my to find the answers for the 4k test data Using md5, they created a hash for each unique pet, and looked up the score for each hash from the external dataset - there were 3500 overlaps Did Pandas column manipulation to get at the hidden prediction variable for every 10th pet and replaces the prediction that should have been generated by the algorithm with the actual value Using mostly: obfuscated functions, Pandas, and dictionaries, as well as MD5 hashes Fallout: He was fired from H20.ai Kaggle issued an apology Michael #3: Configuring uWSGI for Production Deployment We run a lot of uWSGI backed services. I’ve spoken in-depth back on Talk Python 215: The software powering Talk Python courses and podcast about this. This is guidance from Bloomberg Engineering’s Structured Products Applications group We chose uWSGI as our host because of its performance and feature set. But, while powerful, uWSGI’s defaults are driven by backward compatibility and are not ideal for new deployments. There is also an official Things to Know doc. Unbit, the developer of uWSGI, has “decided to fix all of the bad defaults (especially for the Python plugin) in the 2.1 branch.” The 2.1 branch is not released yet. Warning, I had trouble with die-on-term and systemctl Settings I’m using: # This option tells uWSGI to fail to start if any parameter # in the configuration file isn’t explicitly understood by uWSGI. strict = true # The master uWSGI process is necessary to gracefully re-spawn # and pre-fork workers, consolidate logs, and manage many other features master = true # uWSGI disables Python threads by default, as described in the Things to Know doc. enable-threads = true # This option will instruct uWSGI to clean up any temporary files or UNIX sockets it created vacuum = true # By default, uWSGI starts in multiple interpreter mode single-interpreter = true # Prevents uWSGI from starting if it is unable to find or load your application module need-app = true # uWSGI provides some functionality which can help identify the workers auto-procname = true procname-prefix = pythonbytes- # Forcefully kill workers after 60 seconds. Without this feature, # a stuck process could stay stuck forever. harakiri = 60 harakiri-verbose = true Vicki #4: Thinc: A functional take on deep learning, compatible with Tensorflow, PyTorch, and MXNet A deep learning library that abstracts away some TF and Pytorch boilerplate, from Explosion Already runs under the covers in SpaCy, an NLP library used for deep learning type checking, particularly helpful for Tensors: PyTorchWrapper and TensorFlowWrapper classes and the intermingling of both Deep support for numpy structures and semantics Assumes you’re going to be using stochastic gradient descent And operates in batches Also cleans up the configuration and hyperparameters Mainly hopes to make it easier and more flexible to do matrix manipulations, using a codebase that already existed but was not customer-facing. Examples and code are all available in notebooks in the GitHub repo Michael #5: pandas-vet via Jacob Deppen A plugin for Flake8 that checks pandas code Starting with pandas can be daunting. The usual internet help sites are littered with different ways to do the same thing and some features that the pandas docs themselves discourage live on in the API. Makes pandas a little more friendly for newcomers by taking some opinionated stances about pandas best practices. The idea to create a linter was sparked by Ania Kapuścińska's talk at PyCascades 2019, "Lint your code responsibly!" Vicki #6: NumPy beginner documentation NumPy is the backbone of numerical computing in Python: Pandas (which I mentioned before), scikit-learn, Tensorflow, and Pytorch, all lean heavily if not directly depend on its core concepts, which include matrix operations through a data structure known as a NumPy array (which is different than a Python list) - ndarray Anne Bonner wrote up new documentation for NumPy that introduces these fundamental concepts to beginners coming to both Python and scientific computing Before, you went directly to the section about arrays and had to search through it find what you wanted. The new guide, which is very nice, includes a step-by-step on how arrays work, how to reshape them, and illustrated guides on basic array operations. Extras: Vicki I write a newsletter, Normcore Tech, about all things tech that I’m not seeing covered in the mainstream tech media. I’ve written before about machine learning, data for NLP, Elon Musk memes, and Nginx. There’s a free version that goes out once a week and paid subscribers get access to one more newsletter per week, but really it’s more about the idea of supporting in-depth writing about tech. vicki.substack.com Michael: pip 20.0 Released - Default to doing a user install (as if --user was passed) when the main site-packages directory is not writeable and user site-packages are enabled, cache wheels built from Git requirements, and more. Homebrew: brew install python@3.8 Joke: An SEO expert walks into a bar, bars, pub, public house, Irish pub, tavern, bartender, beer, liquor, wine, alcohol, spirits...

Alternate Current Radio Presents: BOILER ROOM - Uninterruptible Talk Radio for Social Rejects and otherwise lovable political animals.Last episode of the year! Hesher, Spore, Jay, Randy and Daniel form up the ACR Brain-trust known as the BOILER ROOM to discuss the latest news near the end of 2019. The cases of Epstein, Weinstein and Kevin Spacey all have new developments floating around the media. Gwyneth Paltrow wants YOU to be a good global citizen and wipe your butt with expensive highly processed TP that costs $34 a package. In contrast, in China the plebs have to use facial recognition technology to be allotted 28" of TP in public restrooms, and its not a far stretch to bet that their social credit score will be attached to this bio-metricly tracked just like everything else they do. Arizona police kick down door of parents who refused to take unvaccinated 2-year-old to hospital. Amazon ‘Alexa’ Tells Terrified Mom to Make Sure She Kills Herself by ‘Stabbing Herself in the Heart’. Will Hunter Biden Jeopardize His Father’s Campaign as he is subject of criminal probes, hinting at more incriminating details? A CGI "Influencer" claimed she was sexually assaulted... all this and more.

Alternate Current Radio Presents: BOILER ROOM - Uninterruptible Talk Radio for Social Rejects and otherwise lovable political animals.Last episode of the year! Hesher, Spore, Jay, Randy and Daniel form up the ACR Brain-trust known as the BOILER ROOM to discuss the latest news near the end of 2019. The cases of Epstein, Weinstein and Kevin Spacey all have new developments floating around the media. Gwyneth Paltrow wants YOU to be a good global citizen and wipe your butt with expensive highly processed TP that costs $34 a package. In contrast, in China the plebs have to use facial recognition technology to be allotted 28" of TP in public restrooms, and its not a far stretch to bet that their social credit score will be attached to this bio-metricly tracked just like everything else they do. Arizona police kick down door of parents who refused to take unvaccinated 2-year-old to hospital. Amazon ‘Alexa’ Tells Terrified Mom to Make Sure She Kills Herself by ‘Stabbing Herself in the Heart’. Will Hunter Biden Jeopardize His Father’s Campaign as he is subject of criminal probes, hinting at more incriminating details? A CGI "Influencer" claimed she was sexually assaulted... all this and more.

Alternate Current Radio Presents: BOILER ROOM - Uninterruptible Talk Radio for Social Rejects and otherwise lovable political animals.Last episode of the year! Hesher, Spore, Jay, Randy and Daniel form up the ACR Brain-trust known as the BOILER ROOM to discuss the latest news near the end of 2019. The cases of Epstein, Weinstein and Kevin Spacey all have new developments floating around the media. Gwyneth Paltrow wants YOU to be a good global citizen and wipe your butt with expensive highly processed TP that costs $34 a package. In contrast, in China the plebs have to use facial recognition technology to be allotted 28" of TP in public restrooms, and its not a far stretch to bet that their social credit score will be attached to this bio-metricly tracked just like everything else they do. Arizona police kick down door of parents who refused to take unvaccinated 2-year-old to hospital. Amazon ‘Alexa’ Tells Terrified Mom to Make Sure She Kills Herself by ‘Stabbing Herself in the Heart’. Will Hunter Biden Jeopardize His Father’s Campaign as he is subject of criminal probes, hinting at more incriminating details? A CGI "Influencer" claimed she was sexually assaulted... all this and more.

SpaCy is awesome for NLP! It’s easy to use, has widespread adoption, is open source, and integrates the latest language models. Ines Montani and Matthew Honnibal (core developers of spaCy and co-founders of Explosion) join us to discuss the history of the project, its capabilities, and the latest trends in NLP. We also dig into the practicalities of taking NLP workflows to production. You don’t want to miss this episode!

SpaCy is awesome for NLP! It’s easy to use, has widespread adoption, is open source, and integrates the latest language models. Ines Montani and Matthew Honnibal (core developers of spaCy and co-founders of Explosion) join us to discuss the history of the project, its capabilities, and the latest trends in NLP. We also dig into the practicalities of taking NLP workflows to production. You don’t want to miss this episode!

According to Ayurvedic Wisdom, the fall winds can make you feel spacy especially if you have a VATA dosha. Learn more as Andrea Trank, certified health coach and yoga teacher explains. --- Send in a voice message: https://anchor.fm/andrea-beth-trank/message

Wir wollten eigentlich eine weitere Einsteigerepisode aufnehmen und waren daher auf der Suche nach einem einfachen Beispiel für die Verwendung der grundlegenden Datenstrukturen. Leider ist das Beispiel doch nicht so supereinfach geworden und so ist das hier dann eher eine Episode zum Thema Naive Bayes. Hmm, auch nicht so uninteressant, wie ich finde :). Shownotes Unsere E-Mail für Fragen, Anregungen & Kommentare: hallo@python-podcast.de News aus der Szene FrOSCon - Deep Learning Workshop Generatoren Coroutinen SHORTHANDED NEWS Django Chat, Episode 23: Async Django - Andrew Godwin Django 3 - Async Roadmap Naive Bayes Naive Bayes (wikipedia) Naive Bayes jupyter notebook Defaultdict Support Vector Machine Word Embeddings SpaCy Techtiefen: SpaCy Techtiefen: Moderne Sprachverarbeitung BERT RoBERTa XLNet gpt-2 AlterEgo Picks isort pptop Öffentliches Tag auf konektom

In der zweiten Podcastfolge der Reihe zu natürlicher Sprachverarbeitung ist Ines Montani zu Gast, Entwicklerin von SpaCy und Mitgründerin von Prodigy. Zunächst sprechen wir generell über den Umgang mit Sprache, warum dieser so komplex ist und wie die open source Bibliothek spaCy hier hilft. Dabei geht es um typische Aufgaben wie Part of Speach Tagging, Lemmatization und Named Entity Recognition genauso wie um geeignete Einsatz-Szenarien in der Industrie. Des Weiteren gibt Ines Einblicke in ihre tägliche Arbeit am open source Tool und erklärt, warum NLP Modelle auch ohne GPU trainierbar sein müssen und Prodigy kein Interesse an den Daten seiner Kunden hat. Letztlich geben wir einen Überblick über das wachsende spaCy Ökosystem, einen Rückblick auf die spaCy in Real Life Konferenz und Ines gewährt einen Ausblick in zukünftige Entwicklungen bei spaCy und Prodigy. SpaCy Online Kurs: https://course.spacy.io/ SpaCy IRL Videos: https://www.youtube.com/playlist?list=PLBmcuObd5An4UC6jvK_-eSl6jCvP1gwXc Coreference Resolution: https://github.com/huggingface/neuralcoref Sci-SpaCy: https://github.com/allenai/scispacy

We are delighted to welcome Ines Montani and Matt Honnibal, the developers of spaCy - a powerful and advanced library for NLP. Everything you've ever wanted to know about the wonderful spaCy library is right here in the latest DataHack Radio podcast. Highlights of topic covered: - The idea behind developing spaCy - spaCy's awesome evolution from the first alpha release to the current version 2.1 - Use cases of spaCy including a couple of surprising applications - Ines and Matt's advice to NLP enthusiasts Read the full article here: https://www.analyticsvidhya.com/blog/2019/05/datahack-radio-ines-montani-matthew-honnibal-brains-behind-spacy

In this episode of PyDataSci, we’re joined by Ines Montani, Cofounder of Explosion, Co-developer of SpaCy and lead developer of Prodigy. Ines and I caught up to discuss her various projects, including the aforementioned SpaCy, an open-source NLP library built with a focus on industry and production use cases. The complete show notes for this episode can be found at twimlai.com/talk/262. Check out the rest of the PyDataSci series at twimlai.com/pydatasci. We want to better understand your views on the importance of open source and the projects and players in this space. To access the survey visit twimlai.com/pythonsurvey. Thanks to this weeks sponsor, IBM, for their support of the podcast! Visit twimlai.com/ibm to learn more about the IBM Data Science Community. 

"There's two big challenges about being visionary. The first challenge is having the foresight to look out in the future and predict," said Maj. Gen. Brad Spacy, AFIMSC commander. "The hardest challenge... is the second part about being visionary... and that's getting people to listen to you." Maj. Gen. Spacy discussed the future of Air Force Installations in the 21st century at the 2019 SAME-IFMA Workshop in San Antonio, Texas, February 7.

We present two stories in this episode.The first story we see a return of The Grey Whisper and Rusty in the beginning of a three part saga: Anthracite in Blue! Someone has stolen some fiber art from the fourth most popular museum in northern Massachusetts. Madeline Forthright and Rusty travel to Athol, Massachusetts and investigate!Then, what would happen if a bunch of Bradys went to space? Find out in the Spacy Squad!

Yummi Yummi-HQ holder sæsonafslutning med besøg af instruktøren bag dokumentarfilmen ‘Gasolin', Anders Østergaard, og sammen med ham gennemgår vi Gasolin's sidst udsendte studiealbum ‘Killin' Time'. Vi når også forbi Starfuckers-pladen ‘Vogt dem for efterligninger' og kommer i øvrigt rundt om en halvdøv producer, indtil flere mord(!), en oldgammel italiensk talemåde, urinalkugler, en julekalender med kun én låge, Kims badevaner, en mageløs telefonsamtale og den kontroversielle karakter på Spacy-skalaen: ‘Høj kælder'!Trackliste:Starfuckers: BlaffersangenStarfuckers: JoannaStarfuckers: Hva' gør vi nu, lille du?Stig Møller & Kim Larsen: SkovsøenGasolin': Girl You Got Me LonelyGasolin': Girl You Got Me Lonely (single-version)Gasolin': Girl You Got Me Lonely (live)Gasolin': CloserGasolin': JailbaitGasolin': Let It FlowGasolin': Snow QueenGasolin': Killin' TimeGasolin': In The WingsGasolin': HighschoolGasolin': StopGasolin': Stop (live)Gasolin': Magic GardenGasolin': U Lu La LuGasolin': Where Do We Go Now, Mon AmiGasolin': Se Venedig og dø (demo)Lone Kellermann feat. Dirch Passer, Tommy Kenter og Kim Larsen: Se Venedig og dø (live)Gasolin': This Is My Life (live)

never gonna giver you upper

Most NLP projects rely crucially on the quality of annotations used for training and evaluating models. In this episode, Matt and Ines of Explosion AI tell us how Prodigy can improve data annotation and model development workflows. Prodigy is an annotation tool implemented as a python library, and it comes with a web application and a command line interface. A developer can define input data streams and design simple annotation interfaces. Prodigy can help break down complex annotation decisions into a series of binary decisions, and it provides easy integration with spaCy models. Developers can specify how models should be modified as new annotations come in in an active learning framework. Prodigy: https://prodi.gy Prodigy recipe scripts: https://github.com/explosion/prodigy-recipes Twitter: https://twitter.com/_inesmontani https://twitter.com/honnibal

On this episode of the Bike Shed Chris is joined by George Brocklehurst, development director in thoughtbot's New York studio. The conversation starts with a discussion around progressive enhancement and the state of the modern web, and then shifts to focus on George's recent explorations of machine learning. This episode is a perfect introduction to the topic of ML, and provides a great summary of why you might want to start working with it and how to go about that. Does Progressive Enhancement Have a Place in Today's Web? Vue.js Electron React Native React Native for Desktop Frameworks and Tools For Exploring Machine Learning NumPy SciPy Jupyter Notebook Pandas scikit-learn Natural Language Toolkit (NLKT) spaCy gensim Getting Started with Machine Learning: Intro to Machine Learning Workshop What is Machine Learning? Named Entity Recognition Recommending blog posts with machine learning

The O’Reilly Programming Podcast: Wrangling data with Python’s libraries and packages.In this episode of the O’Reilly Programming Podcast, I talk with Katharine Jarmul, a Python developer and data analyst whose company, Kjamistan, provides consulting and training on topics surrounding machine learning, natural language processing, and data testing. Jarmul is the co-author (along with Jacqueline Kazil) of the O’Reilly book Data Wrangling with Python, and she has presented the live online training course Practical Data Cleaning with Python.Discussion points: How data wrangling enables you to take real-world data and “clean it, organize it, validate it, and put it in some format you can actually work with,” says Jarmul. Why Python has become a preferred language for use in data science: Jarmul cites the accessibility of the language and the emergence of packages such as NumPy, pandas, SciPy, and scikit-learn. Jarmul calls pandas “Excel on steroids” and says, “it allows you to manipulate tabular data, and transform it quite easily. For anyone using structured, tabular data, you can’t go wrong with doing some part of your analysis in pandas.” She cites gensim and spaCy as her favorite NLP Python libraries, praising them for “the ability to just install a library and have it do quite a lot of deep learning or machine learning tasks for you.” Other links: Check out the video Building Data Pipelines with Python, presented by Jarmul. Check out the video Data Wrangling and Analysis with Python, presented by Jarmul. Jarmul is one of the founders of the group PyLadies, which focuses on helping more women become active participants and leaders in the Python open source community.

The O’Reilly Programming Podcast: Wrangling data with Python’s libraries and packages.In this episode of the O’Reilly Programming Podcast, I talk with Katharine Jarmul, a Python developer and data analyst whose company, Kjamistan, provides consulting and training on topics surrounding machine learning, natural language processing, and data testing. Jarmul is the co-author (along with Jacqueline Kazil) of the O’Reilly book Data Wrangling with Python, and she has presented the live online training course Practical Data Cleaning with Python.Discussion points: How data wrangling enables you to take real-world data and “clean it, organize it, validate it, and put it in some format you can actually work with,” says Jarmul. Why Python has become a preferred language for use in data science: Jarmul cites the accessibility of the language and the emergence of packages such as NumPy, pandas, SciPy, and scikit-learn. Jarmul calls pandas “Excel on steroids” and says, “it allows you to manipulate tabular data, and transform it quite easily. For anyone using structured, tabular data, you can’t go wrong with doing some part of your analysis in pandas.” She cites gensim and spaCy as her favorite NLP Python libraries, praising them for “the ability to just install a library and have it do quite a lot of deep learning or machine learning tasks for you.” Other links: Check out the video Building Data Pipelines with Python, presented by Jarmul. Check out the video Data Wrangling and Analysis with Python, presented by Jarmul. Jarmul is one of the founders of the group PyLadies, which focuses on helping more women become active participants and leaders in the Python open source community.

Space feels on space feels on space feels, unicorn magic, and fuck Trump!

Welcome back where the analysis is fresh and trash is good. Your favorite movie analysis podcast is back to talk F Gary Gray's hostage thriller The Negotiator. Is it a bucket of fun or a bucket of poo? You'll have to tune in to find out our thoughts on this 1998 action flick. But first, we have to play a game. This week, we talk about our favorite "Wrong Men". Arthur thought he had written and closed the book on the subject, but Alex comes in at the last second with arguably one of the better "Wrong Man" characters. As the analysis gets underway, Arthur presents a formalist/genre reading of The Negotiator. He examines how F Gary Gray utilizes the editing, camera and production design to infuse a sense of urgency into a substandard procedural story. Dalton is next. He presents a look at beurocracy and the thin line that is constantly walked by those in the criminal justice system. He connects these dots to beurocracy on a more general scale. Alex piggybacks off of Dalton's reading in presenting her ideas. She reiterates the strength of Dalton's points, before aligning these points with current political affairs. Alex brings up how America's distrust in government has grown. She is brimming with ideas from her recent OK Policy Summit. Dustin takes things home by discussing the myth of redemptive violence. He examines how violence in this form is used to control people. Dustin then compares the approach of both Spacy and Jackson's characters in the film. Well that's it for this week, Dear Listener. Now, if you're ready, go ahead and hit play. And remember, you never tell the hostage taker, "No." The Negotiator Timestamps 00:38 - Welcome and Introductions05:27 - Synopsis and Reviews15:19 - The Game - Favorite Wrong Men22:44 - Social Media24:01 - Analysis50:01 - Shelf or Trash/Else or Instead57:35 - Wrap Up

As the amount of text available on the internet and in businesses continues to increase, the need for fast and accurate language analysis becomes more prominent. This week Matthew Honnibal, the creator of SpaCy, talks about his experiences researching natural language processing and creating a library to make his findings accessible to industry.

Words from Galileo to John Glenn, Archibald MacLeish to Einstein and Emily Dickinson, Ray Bradbury, Leslie Bricusse and Anthony Newley, WallaceStevens, William Cullen Bryant, John Brainard, and Lola Ridge from DUBlin, with music “I Will Survive,” “Happy Days,” “Starman,”“Woodstock,” “Oh … Continue reading → The post IMAGINATION EVERY, EVERY MINUTE: AN ASTRONOMICAL HOUR, OUR SPACY SHOW WHICH SWINGS LIKE A STAR ON THE MARTIAN,CHALLENGER, STAR WARS, LORD OF THE RINGS, PLANETS MARS AND EARTH, AND WHAT POETRY HAS TO DO WITH IT first appeared on Dr. Barbara Mossberg » Poetry Slowdown.

On demarre doucement avec sans doute l'intro la plus longue que j'ai fait sur un PodCast (by Pantha du Prince) La premiere partie (contrairement a la 2eme) est assez pointu mais pas super pechu ! La suite (Part 2) arrive vers le 20 Aout !!! Bonne Vacances à tous ;-) EmmanuelR's Links : iTunes : http://bit.ly/dcZJH8 Mix.Dj : http://mix.dj/EmmanuelR Twitter : http://twitter.com/DjEmmanuelR MixCloud : http://mixcloud.com/EmmanuelR PodOmatic : http://podomatic.com/EmmanuelR FaceBook : http://facebook.com/EmmanuelEre SounCloud 1 : http://soundcloud.com/EmmanuelR SoundCloud 2 : http://soundcloud.com/DjEmmanuelR RA : http://residentadvisor.net/dj/EmmanuelR Mxify : http://mixify.com/DjEmmanuelR ALL BOOKING = NouvelRparis@gmail.com

Track List Kyoto - Dubsahara (Greg Hunter) Unluck - James Blake Latchmere Allotments (Nocturnal Sunshine Remix) - The Orb The Goddess - Skytree Put You Down (Ramadanman Refix) - TRG Poltigiest - Silkie Mama Llama Maestro - Bluetech Middle east dub - King Tubby Monsters - Innerzone Orchestra (Carl Creig) Dark Path - Goth Trad Deep Pools - Ital Tek Deep Into Your Subconcious I Slide - Future Sound Of London Faith In Her - Clubroot Shell Of Light - Burial Once Upon The Sea Of Blissful Awareness (D. D. Corporation Remix) - Shpongle Dem Never Know (Sleeparchive remix) - Rhythm & Sound with Jah Cotton Hither And Zither - Audio Active & Laraaji Memories Of Love - Synkro People Music - Herbie Hancock Mixed By Kluster 2012/8/9 http://www.facebook.com/KLF2013 Kluster's Soundcloud http://bit.ly/Nb98bP Kluster's Mixcloud http://bit.ly/M8baxs

Today on Health in 2 Point 00, It's a whacky SPACy world, as a new SPAC rolls “UpHealth” and has me singing Bob Marley, meanwhile there's $91m for remote clinical trial software player Medable, $76m more for Spring Health joining the throng of mental health companies, while K-Health gets $42m to introduce chat bot front ends to Korean pop music…or something like that.