Podcasts about scikit

In this podcast episode, we talked with Tamara Atanasoska about ​building fair AI systems. About the Speaker: ​Tamara works on ML explainability, interpretability and fairness as Open Source Software Engineer at probable. She is a maintainer of fairlearn, contributor to scikit-learn and skops. Tamara has both computer science/ software engineering and a computational linguistics(NLP) background. During the event, the guest discussed their career journey from software engineering to open-source contributions, focusing on explainability in AI through Scikit-learn and Fairlearn. They explored fairness in AI, including challenges in credit loans, hiring, and decision-making, and emphasized the importance of tools, human judgment, and collaboration. The guest also shared their involvement with PyLadies and encouraged contributions to Fairlearn. 0:00 Introduction to the event and the community 1:51 Topic introduction: Linguistic fairness and socio-technical perspectives in AI 2:37 Guest introduction: Tamara's background and career 3:18 Tamara's career journey: Software engineering, music tech, and computational linguistics 9:53 Tamara's background in language and computer science 14:52 Exploring fairness in AI and its impact on society 21:20 Fairness in AI models 26:21 Automating fairness analysis in models 32:32 Balancing technical and domain expertise in decision-making 37:13 The role of humans in the loop for fairness 40:02 Joining Probable and working on open-source projects 46:20 Scopes library and its integration with Hugging Face 50:48 PyLadies and community involvement 55:41 The ethos of Scikit-learn and Fairlearn

We are at GenAI saturation, so let's talk about scikit-learn, a long time favorite for data scientists building classifiers, time series analyzers, dimensionality reducers, and more! Scikit-learn is deployed across industry and driving a significant portion of the "AI" that is actually in production. :probabl is a new kind of company that is stewarding this project along with a variety of other open source projects. Yann Lechelle and Guillaume Lemaitre share some of the vision behind the company and talk about the future of scikit-learn!

We are at GenAI saturation, so let's talk about scikit-learn, a long time favorite for data scientists building classifiers, time series analyzers, dimensionality reducers, and more! Scikit-learn is deployed across industry and driving a significant portion of the "AI" that is actually in production. :probabl is a new kind of company that is stewarding this project along with a variety of other open source projects. Yann Lechelle and Guillaume Lemaitre share some of the vision behind the company and talk about the future of scikit-learn!

Plongez dans l'univers de l'IA avec Gaël Varoquaux, chercheur à l'Inria, Co-fondateur de scikit-learn et ProbablComment un chercheur transforme la science des données et révolutionne le machine learning ? Dans cet épisode passionnant, Gaël Varoquaux, co-fondateur de Scikit-learn et pionnier de l'intelligence artificielle, nous raconte son parcours, de ses débuts en open-source à ses initiatives pour un futur de l'IA plus éthique et durable. Entre anecdotes inspirantes et perspectives éclairantes, découvrez les défis et les ambitions derrière la création de Probabl et le rôle croissant de l'IA dans les sciences sociales.(00:00) - Introduction à Scikit-Learn et à Gaël Varoquaux (02:51) - Parcours de Gaël Varoquaux et ses contributions (06:02) - Recherche fondamentale vs appliquée (08:57) - L'impact de Scikit-Learn sur la communauté (12:06) - Différences entre machine learning et intelligence artificielle (15:04) - Transition vers l'entrepreneuriat avec :Probabl (17:52) - Soda : IA au service des sciences sociales (20:58) - Perspectives sur l'avenir de l'IA et du machine learning (24:07) - Conclusion et réflexions finales

Scikit-learn's documentation pages are celebrated. But not everyone is aware that the project actually has somebody on payroll to take care of it. In this episode we talk to Arturo about stories from the scikit-learn documentation. In particular, the docs have a recommender that few folks are aware of. People just assume that it is manually curated, but there are a few base scikit-learn tools under the hood there. Link to the official scikit-learn MOOC: https://inria.github.io/scikit-learn-mooc/ We have a Discord these days, feel free to discuss the podcast with us there! https://discord.probabl.ai You can follow the podcast on most podcast players including apple podcasts, spotify and rss.com. - https://podcasts.apple.com/us/podcast/sample-space/id1739598572 - https://open.spotify.com/show/0BnwEHuyOlHgeZfselpn1n - https://rss.com/podcasts/sample-space/ This podcast is part of the open efforts over at probabl. To learn more you can check out website or reach out to us on social media. Website: https://probabl.ai/ Bluesky: https://bsky.app/profile/probabl.bsky.social LinkedIn: https://www.linkedin.com/company/probabl Twitter: https://x.com/probabl_ai

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And it's this time of the year again, when the RSE communities meet for their annual gathering. The UK RSE Society held its annual conference in the Welsh city of Swansea this year. And in this episode you'll hear from a range of different participants and presenters. Here they are in the following orderJamie Quinn (University College London and trustee of the Society until this year) https://www.ucl.ac.uk/advanced-research-computing/advanced-research-computing-centre Gael Varoquaux from Inria and Scikit-learn in France https://scikit-learn.org/stable/ Neil Chue Hong from the Software Sustainability Institute https://www.software.ac.uk Sarah Gibson from https://2i2c.org Hannah Williams from the UK Health Security Agency https://ukhsa.blog.gov.uk Rich Pitts from Oracle Research https://www.oracle.com/uk/research/ Milo Thurnston from https://fairsharing.org Becky Smith from the organising committee https://rsecon23.society-rse.org/conference-committee/ Presentations have been streamed and should be accessible soon.Support the Show.Thank you for listening and your ongoing support. It means the world to us! Support the show on Patreon https://www.patreon.com/codeforthought Get in touch: Email mailto:code4thought@proton.me UK RSE Slack (ukrse.slack.com): @code4thought or @piddie US RSE Slack (usrse.slack.com): @Peter Schmidt Mastadon: https://fosstodon.org/@code4thought or @code4thought@fosstodon.org LinkedIn: https://www.linkedin.com/in/pweschmidt/ (personal Profile)LinkedIn: https://www.linkedin.com/company/codeforthought/ (Code for Thought Profile) This podcast is licensed under the Creative Commons Licence: https://creativecommons.org/licenses/by-sa/4.0/

Julien Hillairet est titulaire d'un doctorat en électronique et micro-ondes délivré par l'Université de Toulouse en 2007. Il travaille depuis au Commissariat à l'énergie atomique et aux énergies alternatives (CEA) situé à Cadarache, en France. Julien est spécialisé dans les systèmes radiofréquences de haute puissance et les interactions plasma/ondes bien que ses recherches portent également sur les matériaux et la tribologie dans le domaine des radiofréquences, ainsi que sur les multipactors. L'une de ses contributions remarquables au domaine est le développement d'un code de couplage plasma/onde à open-source appelé ALOHA.  Au-delà de sa productivité dans le monde de la recherche, Julien est également le développeur principal du package python open source Scikit-rf pour l'ingénierie des micro-ondes et des radiofréquences.   URL pour les notes de l'épisode : https://www.podcastics.com/podcast/episode/fusion-nucleaire-sommes-nous-a-la-veille-dune-revolution-248771/     [01:12] Chapitre 1 : La fusion nucléaire, l'énergie des étoiles? [23:35] Chapter 2 : La technologie de fusion [01:14:53] Chapitre 3 : La centrale nucléaire à fusion, c'est pour quand? [01:22:31] Chapitre 4 : A propos de Julien   Inscrivez-vous pour les dernières updates du podcast exergie: http://eepurl.com/hVeLPz This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Train your own AI using this free Lab created by Dr Mike Pound. Big thanks to Brilliant for sponsoring this video! Get started with a free 30 day trial and 20% discount: https://brilliant.org/DavidBombal How do you capitalize on this trend and learn AI? Dr Mike Pound of Computerphile fame shows us practically how to train your own AI. And the great news is that he has shared his Google colab lab with us to you can start learning for free! If you are into cybersecurity or any other tech field, you probably want to learn about AI and ML. They can really help your resume and help you increase the $$$ you earn. Machine Learning / Artificial Intelligence is a fantastic opportunity for you to get a better job. Start learning this amazing technology today and start learning with one of the best! // LAB // Go here to access the lab: https://colab.research.google.com/dri... // Previous Videos // Roadmap to ChatGPT and AI mastery: • Roadmap to ChatGP... I challenged ChatGPT to code and hack: • I challenged Chat... The truth about AI and why you should learn it - Computerphile explains: • The truth about A... // Dr Mike's recommend AI Book // Deep learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville: https://amzn.to/3vmu4LP // Dawid's recommend Books // 1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: https://amzn.to/3IrGCHi 2. Pattern Recognition and Machine Learning: https://amzn.to/3IWVm2v 3. Machine Learning: A Probabilistic Perspective: https://amzn.to/3xYFM05 4. Python Machine Learning: https://amzn.to/3y0r08Q 5. Deep Learning: https://amzn.to/3kxSbVu 6. The Elements of Statistical Learning: https://amzn.to/3Iwuuox 7. Linear Algebra and Its Applications: https://amzn.to/3EGwMAs 8. Probability Theory: https://amzn.to/3IrGeZm 9. Calculus: Early Transcendentals: https://amzn.to/3Z3Eugh 10. Discrete Mathematics with Applications: https://amzn.to/3Zpzpyt 11. Mathematics for Machine Learning: https://amzn.to/3m8jp5N 12. A Hands-On Introduction to Data Science: https://amzn.to/3Szob8c 13. Introduction to Algorithms: https://amzn.to/3xXo50K 14. Artificial Intelligence: https://amzn.to/3Z2fqGv // Courses and tutorials // AI For Everyone by Andrew Ng: https://www.coursera.org/learn/ai-for... PyTorch Tutorial From Research to Production: https://www.infoq.com/presentations/p... Scikit-learn Machine Learning in Python: https://scikit-learn.org/stable/ // PyTorch // Github: https://github.com/pytorch Website: https://pytorch.org/ Documentation: https://ai.facebook.com/tools/pytorch/ // Mike SOCIAL // Twitter: https://twitter.com/_mikepound YouTube: / computerphile Website: https://www.nottingham.ac.uk/research... // David SOCIAL // Discord: https://discord.com/invite/usKSyzb Twitter: https://www.twitter.com/davidbombal Instagram: https://www.instagram.com/davidbombal LinkedIn: https://www.linkedin.com/in/davidbombal Facebook: https://www.facebook.com/davidbombal.co TikTok: http://tiktok.com/@davidbombal YouTube: / davidbombal // MY STUFF // https://www.amazon.com/shop/davidbombal // SPONSORS // Interested in sponsoring my videos? Reach out to my team here: sponsors@davidbombal.com Please note that links listed may be affiliate links and provide me with a small percentage/kickback should you use them to purchase any of the items listed or recommended. Thank you for supporting me and this channel! #chatgpt #computerphile #ai

ChatGPT and AI mastery - how to get started in AI. Big thanks to Brilliant for sponsoring this video! Get started with a 20% discount using this link: https://brilliant.org/davidbombal How do you capitalize on this trend and learn AI? Dr Mike Pound of Computerphile fame tells us how to ride this wave. If you are into cybersecurity or any other tech field, you probably want to learn about AI and ML. They can really help your resume and help you increase the $$$ you earn. AI just become Sentient? And will it take your job? Or is AI just a fantastic opportunity for you to get a better job? In this interview with Dr Michael Pound we discuss hype vs reality and get a quick start guide on how to learn AI. // MENU // 00:00 - Coming up 00:40 - Sponsored segment 02:28 - A.I. Hype // Should we be worried? 03:37 - Amazing but flawed 08:07 - Is it worth it getting into CompSci? 10:02 - Knowing A.I. makes you valuable // Learn A.I. 13:43 - Resources for learning A.I. 15:58 - Should you get into CompSci? 17:35 - Enhancing your career with A.I. 20:16 - The limits of A.I. 24:57 - A.I in academics // How A.I. affects academic work 31:02 - Conclusion // Previous Videos // I challenged ChatGPT to code and hack: https://youtu.be/Fw5ybNwwSbg The truth about AI and why you should learn it - Computerphile explains: https://youtu.be/PH9RQ6Yx75c // BOOK // Deep learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville: https://amzn.to/3vmu4LP // Courses and tutorials // AI For Everyone by Andrew Ng: https://www.coursera.org/learn/ai-for... PyTorch Tutorial From Research to Production: https://www.infoq.com/presentations/p... Scikit-learn Machine Learning in Python: https://scikit-learn.org/stable/ // PyTorch // Github: https://github.com/pytorch Website: https://pytorch.org/ Documentation: https://ai.facebook.com/tools/pytorch/ // Mike SOCIAL // Twitter: https://twitter.com/_mikepound YouTube: https://www.youtube.com/user/Computer... Website: https://www.nottingham.ac.uk/research... // David SOCIAL // Discord: https://discord.com/invite/usKSyzb Twitter: https://www.twitter.com/davidbombal Instagram: https://www.instagram.com/davidbombal LinkedIn: https://www.linkedin.com/in/davidbombal Facebook: https://www.facebook.com/davidbombal.co TikTok: http://tiktok.com/@davidbombal YouTube: https://www.youtube.com/davidbombal // MY STUFF // https://www.amazon.com/shop/davidbombal // SPONSORS // Interested in sponsoring my videos? Reach out to my team here: sponsors@davidbombal.com chatgpt chatgpt hype chatgpt reality chatgpt truth ai chatgpt c chatgpt python chatgpt hak5 chatgpt rubber ducky chatgpt cisco python android samsung linux kali linux rubber ducky hak5 omg cable lamda neural network machine learning deep learning sentient google ai mike pound michael pound dr michael pound computerphile artificial intelligence google ai sentient google ai lamda google ai sentient conversation google ai alive ai jobs Please note that links listed may be affiliate links and provide me with a small percentage/kickback should you use them to purchase any of the items listed or recommended. Thank you for supporting me and this channel! #chatgpt #computerphile #ai

https://larsmuellensiefen.substack.com/ - Data Engineering ist ein wichtiger Bestandteil des Prozesses der Datenverarbeitung, der sich mit der Gewinnung, Vorbereitung, Verarbeitung und Verwaltung von Daten beschäftigt. Es gibt viele Python-Pakete, die für die Unterstützung von Data-Aufgaben entwickelt wurden und die es ermöglichen, Daten effektiv zu verarbeiten und zu analysieren. Einige dieser wichtigen Pakete sind Pandas, NumPy, Scikit-learn, TensorFlow, PySpark, Airflow, Dask und SQLAlchemy.

This interview was recorded for the GOTO Book Club.gotopia.tech/bookclubRead the full transcription of the interview hereHolden Karau - Co-Author of "Kubeflow for Machine Learning" & Open Source Engineer at NetflixAdi Polak - VP of Developer Experience at Treeverse & Contributing to lakeFS OSSDESCRIPTIONMachine Learning has been declared dead several times but that's far from true. Join Adi Polak, vice president of developer experience at Treeverse, and Holden Karau, open source engineer at Netflix, in their conversation about Kubeflow and how it provides better tooling in the ML space. The discussion touches on Holden's book “Kubeflow for Machine Learning” and expands to cover the worlds of Ray and Dask.RECOMMENDED BOOKSHolden Karau, Trevor Grant, Boris Lublinsky, Richard Liu & Ilan Filonenko • Kubeflow for Machine LearningHolden Karau • Distributed Computing 4 KidsHolden Karau • Scaling Python with DaskHolden Karau & Boris Lublinsky • Scaling Python with RayHolden Karau & Rachel Warren • High Performance SparkHolden Karau, Konwinski, Wendell & Zaharia • Learning SparkHolden Karau & Krishna Sankar • Fast Data Processing with Spark 2nd EditionHolden Karau • Fast Data Processing with Spark 1st EditionAdi Polak • Machine Learning with Apache SparkPhil Winder • Reinforcement LearningAurélien Géron • Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlowTwitterLinkedInFacebookLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket at gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted almost daily.Discovery MattersA collection of stories and insights on matters of discovery that advance life...Listen on: Apple Podcasts Spotify Health, Wellness & Performance Catalyst w/ Dr. Brad CooperLooking for a catalyst to optimize your health, wellness & performance? You've found it!!Listen on: Apple Podcasts Spotify

Guest Jin Guo | Jinghui Cheng Panelists Richard Littauer | Eriol Fox | Memo Esparza Show Notes Hello and welcome to Sustain Open Source Design! The podcast where we talk about sustaining open source with design. Learn how we, as designers, interface with open source in a sustainable way, how we integrate into different communities, and how we as coders, work with other designers. Today, we have two amazing guests, Jin Guo and Jinghui Cheng from Montréal. Jin is an Assistant Professor at McGill University in the School of Computer Science, and she received her Ph.D. from the University of Notre Dame. She is particularly interested in the intersection between Software Engineering, Human-Computer Interaction, and Artificial Intelligence. Jinghui is an Assistant Professor at Polytechnique Montréal, where he directs the Human Centered Design Lab. His research combines the field of Human-Computer Interaction with Software Engineering. Today, we hear about Jin's grant she received from the Sloan Foundation, which is supporting the open source usability for scientific software. Jin and Jinghui go in depth about things scientific researchers use, some common problems around usability, the different research methods they are using in their studies, and how they incorporate the community aspect to their research. Also, they share advice on how to get involved with research happening on open source. Go ahead and download this episode now to learn more! [00:03:45] Jin explains more about the grant they were given from the Sloan Foundation. [00:05:49] Find out what kinds of open source code scientific researchers use and some common problems around usability. [00:09:04] Jin and Jinghui tell us about the different research methods they are doing. [00:12:41] Richard wonders what Jin and Jinghui are particularly interested in learning from their study that will help their future research and what are they trying to learn on an academic sense. [00:17:47] Eriol wonders if Jin and Jinghui had similar challenges when researching open source projects. [00:22:15] Jin and Jinghui share their thoughts on incorporating the community aspect to their research. [00:25:32] Richard wonders if Jin and Jinghui can share any ideas to designers, communities which have design focus, or open source in general, on how they can get involved with research happening on open source, besides reading papers, doing a PhD, or going to their workshops. [00:28:11] Eriol asks how Jinghui views end users as a kind of designer and what that might mean for how he's doing his work, and if these workshops are a way of doing that. [00:30:25] Jin expands more on her interest in AI and how that's going to work, and how she's going to get AI to play with designers and open source communities. [00:34:10] Find out where you can follow Jin and Jinghui on the internet. Quotes [00:05:16] “We're hoping to use this grant to help advance scientific software usability, but also use the end result from our projects to benefit open source usability as a whole.” [00:15:40] “For open source usability, I think the tooling is one aspect, but the ultimate goal for our improvement on the tooling is the mindset improvement.” [00:18:38] “As a researcher, ideally we would need to make more frequent and iterative collaborations with open source projects by either interviewing them or having scientific project ideas. Balancing with them and to see what is the relevance of our research with their real concerns.” [00:19:22] “One of the things we are currently planning on is to conduct some of the workshops that are going to invite the end users and the designers to be in the same place, to work together to observe their dynamics of communicating.” [00:23:47] “What we hope is to learn the boundary of communication between those more stereotyped communities, but to make them feel welcomed to communicate with each other regardless of their title or role.” [00:27:46] “Design conferences, they need to welcome more people rather than just really fashi fashionable flashy designers doing, well I don't know, stuff for evil clients.” (Eriol) Spotlight [00:35:36] Memo's spotlight is Jamstack. [00:35:57] Eriol's spotlights are FOSS Backstage and one of their favorite academia papers called, “Non-response, Social Exclusion, and False Acceptance: Gatekeeping Tactics and Usability Work in Free-Libre Open Source Software Development,” by Mikko Rajanen, Netta Iivari, and Arto Lanamäki [00:37:17] Richard's spotlight is JS Montreal. [00:37:41] Jinghui's spotlights are projects that influenced him and his research which are Atom, Jupyter notebook, and PyTorch. [00:38:44] Jin's spotlights are two projects that influenced her previous work and current work which are scikit-learn and Zotero. Links Open Source Design Twitter (https://twitter.com/opensrcdesign) Open Source Design (https://opensourcedesign.net/) Sustain Design & UX working group (https://discourse.sustainoss.org/t/design-ux-working-group/348) Sustain Open Source Twitter (https://twitter.com/sustainoss?lang=en) Richard Littauer Twitter (https://twitter.com/richlitt?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor) Eriol Fox Twitter (https://twitter.com/EriolDoesDesign?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor) Memo Esparza Twitter (https://twitter.com/memo_es_) Jin L.C. Guo Twitter (https://twitter.com/jin_lc_guo?lang=en) Jin L.C. Guo Website (http://jguo-web.com/) Jinghui Cheng Twitter (https://twitter.com/jinghuicheng?lang=en) Jinghui Cheng Website (https://jhcheng.me/) Jinghui Cheng Linkedin (https://www.linkedin.com/in/jinghuicheng/) Alfred P. Sloan Foundation (https://sloan.org/) Argumentation theory (https://en.wikipedia.org/wiki/Argumentation_theory) Jamstack (https://jamstack.org/) FOSS Backstage 2022 (https://foss-backstage.de/) “Non-response, Social Exclusion, and False Acceptance: Gatekeeping Tactics and Usability Work in Free-Libre Open Source Software Development,” by Mikko Rajanen, Netta Iivari, and Arto Lanamäki (https://link.springer.com/chapter/10.1007%2F978-3-319-22698-9_2) JS-Montreal (https://js-montreal.org/) Scikit-learn (https://scikit-learn.org/stable/) Zotero (https://www.zotero.org/) Atom (https://atom.io/) Jupyter (https://jupyter.org/) PyTorch (https://pytorch.org/) Credits Produced by Richard Littauer (https://www.burntfen.com/) Edited by Paul M. Bahr at Peachtree Sound (https://www.peachtreesound.com/) Show notes by DeAnn Bahr Peachtree Sound (https://www.peachtreesound.com/) Special Guests: Jinghui Cheng and Jin L.C. Guo.

How do you build and maintain a complex suite of Python packages? Of course, you want to put them on PyPI. The best format there is as a wheel. This means that when developers use your code, it comes straight down and requires no local tooling to install and use. But if you have compiled dependencies, such as C or FORTRAN, then you have a big challenge. How do you automatically compile and test against Linux, macOS (Intel and Apple Silicon), Windows, and so on? That's the problem cibuildwheel is solving. On this episode, you'll meet Henry Schreiner. He is developing tools for the next era of the Large Hadron Collider (LHC) and is an admin of Scikit-HEP. Of course, cibuildwheel is central to this process. Links from the show Henry on Twitter: @HenrySchreiner3 Henry's website: iscinumpy.gitlab.io Large Hadron Collider (LHC): home.cern cibuildwheel: github.com plumbum package: plumbum.readthedocs.io boost-histogram: github.com vector: github.com hepunits: github.com awkward arrays: github.com Numba: numba.pydata.org uproot4: github.com scikit-hep developer: scikit-hep.org pypa: pypa.io CLI11: github.com pybind11: github.com cling: root.cern Pint: pint.readthedocs.io Python Wheels site: pythonwheels.com Build package: pypa-build.readthedocs.io Mac Mini Colo: macminicolo.net scikit-build: github.com plotext: pypi.org Code Combat: codecombat.com clang format wheel: github.com cibuildwheel examples: cibuildwheel.readthedocs.io Cling in LLVM: root.cern New htmx course: talkpython.fm/htmx Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Talk Python Training AssemblyAI

話した内容Blog このポッドキャストでは、Kaggleを中心としたデータサイエンスに関連する情報を配信していきます。 今回は、Scikit-learn1.0リリース、KaggleのNotebookなどUIの更新、9月の目標結果、今週の分析コンペ、雑談・来週話したいこと について話しました。

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. 2011: Fabian Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, Mathieu Blondel, Gilles Louppe, P. Prettenhofer, Ron Weiss, Ron J. Weiss, J. Vanderplas, Alexandre Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay Keywords: Machine learning, Documentation, Python, scikit-learn, Unsupervised learning, High-level programming language, High- and low-level, Usability, BSD, Binary file, Hypertext Transfer Protocol, Consistency model, Application programming interface, Algorithm, General-purpose markup language https://arxiv.org/pdf/1201.0490v4.pdf

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. 2011: Fabian Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, Mathieu Blondel, Gilles Louppe, P. Prettenhofer, Ron Weiss, Ron J. Weiss, J. Vanderplas, Alexandre Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay Keywords: Machine learning, Documentation, Python, scikit-learn, Unsupervised learning, High-level programming language, High- and low-level, Usability, BSD, Binary file, Hypertext Transfer Protocol, Consistency model, Application programming interface, Algorithm, General-purpose markup language https://arxiv.org/pdf/1201.0490v4.pdf

If you think that knowing Tensorflow and Scikit-learn is enough, think again. MLOps is one of those trendy terms today. What is MLOps and why is it important? In this episode I speak about the undeniable evolution of the data scientist in the last 5-10 years. Sponsors If building software is your passion, you'll love ThoughtWorks Technology Podcast. It's a podcast for techies by techies. Their team of experienced technologists take a deep dive into a tech topic that's piqued their interest — it could be how machine learning is being used in astrophysics or maybe how to succeed at continuous delivery.   Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy. Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.            

Fredrik, Sandra Lindberg och Martin Bagge snackar fönsterhantering, flikar, bokmärken och annat som hör vardagen som datoranvändare till. Sandra har ett inspirerande välordnat system för var fönster hamnar, i vilken ordning de ligger, och hur länge de är öppna. Martin och Fredrik har ambitioner, men når inte fullt så långt. Samma sak gäller hantering av flikar och bokmärken i webbläsare; Sandra har full koll och ett system som håller, Martin har verktyg för att hantera massvis av flikar, och Fredrik försöker hålla flikarna nere men fördelar dem över två webbläsare. Vi diskuterar också trevliga eller nästan omistliga verktyg för att sköta om sina fönster och flikar, och hinner dessutom diskutera filer på skrivbordet en kort sväng. Vi hoppas och tror att ni som lyssnar också har intressanta system - eller intressant oreda utan system - som ni vill dela med er av! Antingen i Slack, eller som en del av ett kommande avsnitt. Vi känner inte att vi hittade några slutliga svar, och det finns massor kvar att diskutera! Avsnittet sponsras av Attentec - oberoende experter på IOT som vill bli fler. Surfa in på attentec.se om du vill veta mer. Fredrik har snackat med Christoffer som jobbar på Attentec med AI och maskininlärning om vad han gör och var AI är på väg. Ett utdrag kommer mitt i avsnittet, och hela snacket finns med som bonusmaterial i avsnittets slut. Ett stort tack till Cloudnet som sponsrar vår VPS! Har du kommentarer, frågor eller tips? Vi är @kodsnack, @tobiashieta, @oferlund, och @bjoreman på Twitter, har en sida på Facebook och epostas på info@kodsnack.se om du vill skriva längre. Vi läser allt som skickas. Gillar du Kodsnack får du hemskt gärna recensera oss i iTunes! Du kan också stödja podden genom att ge oss en kaffe (eller två!) på Ko-fi, eller handla något i vår butik. Länkar Sandra Lindberg Martin Bagge The windows of Siracusa county - det viktiga snacket börjar vid 1:29:43 Hel- och halvskärmsfunktioner i Macos Openbox LXDE KDE KDE 4-övergången Panorama i Firefox Containers i Firefox Windowshade - minimera fönster i Macos till “pinnar” (bild) Magnet Sublime Moom Altdrag Iterm Flycut Attentec - veckans sponsor IOT - Internet of things Christoffer - jobbar på Attentec med AI och maskininlärning Scikit-learn Djupinlärning Pytorch Tensorflow Keras - wrapper för Tensorflow Instapaper Kanbanflow Bitbucket Keywords i Firefox för sökning Marie Kondo Stacks i Macos Länkar för Attentec-snacket Christoffer Johansson Attentec IOT - Internet of things Tekniska verken i Linköping Feature extraction Neurala nät Scikit-learn Djupinlärning Pytorch Tensorflow Keras - wrapper för Tensorflow Nick Bostrom Titlar Skräplådan man har hemma Fokus på en sak i taget På slaskskrivbordet Min väldigt stationära dator Hopp åt sidan som man borde Här finns mitt fönster Team sticks Ordning även på slaskskärmen Fönstren i Partille kommun Anteckningar som inte är kod Jag har fyra flikar Vill jag läsa det här? En permanent bokmärkesplats Om jag inte har någon aning så är det inte viktigt En Marie Kondo för digitalt liv Jag hade också en NAS i ett hörn

TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models --- Send in a voice message: https://anchor.fm/tonyphoang/message

Craig Martell shares the biggest mistakes leaders of ML teams make, what to do if you have no experience leading an ML team, key skills your ML team needs, plus different models/approaches to building an ML team. You’ll hear the most expensive and time-consuming parts of ML, how to estimate timelines, unique tech debt, and how to manage expectations. “If I had to give one piece of advice about starting AI in your company, one of the first people I would hire is a really great data scientist, even if they can't code. Just so they're the one who's going to start training you and helping you think about, how to gather data, how the modeling is going to work, what you're going to need, whether that feature that you want to build is even modelable in the first place..." - Craig Martell Craig is Head of Lyft Machine Learning. He’s also an adjunct professor of Machine Learning for Northeastern University’s Align program. Prior to joining Lyft, he was Head of Machine Intelligence @ Dropbox, and led a number of AI teams and initiatives at LinkedIn, including the development of the LinkedIn AI Academy. Before LinkedIn, Craig was a tenured computer science professor at the Naval Postgraduate School specializing in natural language processing (NLP). He has a Ph.D. in Computer Science from the University of Pennsylvania and is the co-author of the MIT Press book Great Principles of Computing. RESOURCES (ML training) Galvanize: https://bit.ly/3ckF6Gz (website) Andrew Ng: https://bit.ly/3cgerdU (courses) ML: https://bit.ly/3iNB9gf | AI for Everyone: https://bit.ly/2HeZKwl (course) Fast.AI: https://bit.ly/35SPwMD (book) "Hands-On ML with Scikit" : https://amzn.to/35SbGyh SHOW NOTES An overview of the machine learning lifecycle (2:49) The most expensive and time-consuming aspect of the machine learning lifecycle (6:07) The key skills of a machine learning team (7:21) How do you build an AI/ML Team and what are the different models? (8:41) What to do If you’re an engineering manager with no AI/ML skills or experience (15:19) How deep does your understanding of AI/ML have to be in order to lead effectively? (18:48) How do you estimate project timelines for AI/ML teams? (19:15) What are the biggest mistakes engineering leaders make managing AI/ML teams? (20:52) How do you manage expectations in an organization that’s in the early days of AI/ML development? (21:33) What are sources of technical debt unique to AI/ML systems? (22:13) How do machine learning teams interface with product teams? (23:55) AI/ML resources for executive engineering leaders (25:44) When’s the right time to invest in AI/ML? (26:10) Can you apply the Pareto Principle (80/20 rule) to AI/ML development? (26:40) Takeaways (28:12) ELC SUMMIT 2020 Accelerate your growth as an engineering leader at the ELC Summit! Learn from 100+ incredible speakers. Talks cover tons of well-rounded curated topics. There will be opportunities for hands-on practice through workshops (+ other programs), and speed networking with other eng leaders through our own custom-built platform! Details & tickets @ http://elcsummit.com Join our community of software engineering leaders @ https://sfelc.com/ --- Send in a voice message: https://anchor.fm/engineeringleadership/message

Ведущие: * @kalashnikovisme ( https://vk.com/kalashnikovisme ) * @wolffyj ( http://vk.com/wolffyj ) Гость: Ренат Аббязов ( https://vk.com/id236557115 ) ************* Темы выпуска: ************* * Расспрашиваем гостя * Что делают DataScience-специалисты? * Что такое интерактивное телевидение? * Что такое эвристика? * Что такое Hadoop? * Что такое Solid state? * Куда идти учиться? * Как DataScience-специалисты доказывают свою правоту? * Сколько они зарабатывают? * Как погружаться в предметные области? * Что такое TensorFlow? * Как правильно выбирать инструмент для работы? * Сколько процентов времени DataScience-специалисты пишут код? * Когда данные становятся большими? * Мысленный эксперимент про шахматы * Мысленный эксперимент про Dota * Теория игр ------------------ Интересные ссылки: ------------------ * Spark https://spark.apache.org/ * Tensorflow https://www.tensorflow.org/ * Pytorch https://pytorch.org/ * Scikit-learn https://scikit-learn.org/stable/ * Jupyter https://jupyter.org/ * PyCharm https://www.jetbrains.com/pycharm/ * Dask https://dask.org/ * Pandas https://pandas.pydata.org/ * Automl https://cloud.google.com/automl * GPT3 https://arxiv.org/abs/2005.14165 * https://www.bbc.com/news/world-asia-china-43751276 Курсы ----- * https://yandexdataschool.ru/ * https://ru.coursera.org/specializations/machine-learning-data-analysis * https://praktikum.yandex.ru/data-scientist * https://data.mail.ru/pages/index/

PyCon US go online! Subscribe to their YouTube channel to catch all the talks PyLadies also has YouTube Channel Python Ireland's 1st meetUp is tonight talking about App Performance Monitoring in Python. PyCon Australia and PyCon Africa are also going online, details to be announced. Scikit-image join NUMFocus! PyPI Highlight: Foxdot - Make music with Python FastAPI - a high-performance framework, easy to learn, and fast to code

Wat als je grootgebruiker bent van de Python package scikit-learn, maar je merkt dat jij en je collega's regelmatig handmatig scriptjes maken om iets op te lossen wat niet out-of-the-box in scikit-learn voorkomt? Dan bouw je toch je eigen Python package! In aflevering 15 van seizoen 2 spreken Jurjen en Stephanie met Matthijs Brouns en Vincent Warmerdam, makers van de python package scikit-lego. Scikit-lego is een open source package bestaande uit 'legoblokjes' die data scientists en machine learning engineers kunnen gebruiken in hun projecten. Dit zijn bijvoorbeeld blokjes voor het transformeren of modelleren van data, maar ook om meer 'fairness' toe te voegen in een model. Scikit-lego is ongeveer een jaar oud en heeft op dit moment 28 contributors van over de hele wereld, 300 commits en 2.500 downloads per maand. En dat is zo gaaf aan het bouwen en onderhouden van een open source project; dat mensen die je niet kent je package gaan gebruiken, maar ook hieraan kunnen bijdragen en de package beter kunnen maken. Een leuke toevoeging: als je (net als in Python) from scikit-lego import this uitvoert, krijg je een gedichtje waarin onder andere wordt aangegeven dat scikit-lego geen formele banden heeft met zowel scikit-learn als Lego. :) Matthijs Brouns is data science trainer bij Xccelerated en co-chair van PyData Amsterdam. Twitter: @fishnets88 Vincent Warmerdam is research advocate bij Rasa en co-founder van PyData Amsterdam. Twitter: @MatthijsBrs

The machine learning technique is used from market prediction using crypto trading bot These options were set out as explained here Free trading books https://quantlabs.net/ or learn algo trading https://quantlabs.net/dvd Some forecasting methods from an expert: Maybe regress them all against price, or conduct PCA or perhaps you just throw them all into a random forest in order to see which ones are the most important? Where to find these options with Scikit-learn and Python. Scikit-learn seems to to be the simplest machine learning to go with from Python

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre rodar modelos em larga escala. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre matrizes de similaridade. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre Imputação dos Dados e Random Projections. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre Pré-processamento dos Dados. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Macarena Beigier-Bompadre has very deep domain expertise in Machine Learning. Her Data Science skills include Python, R, SQL, Biostatistics, Scikit-learn, Neural Networks, and Deep Learning, and data visualization tools. She co-authored 23 scientific papers. In this episode of the AI Guild Podcast, we talk about the transition from academia into data science consulting Interviewed by Leyla Allahyarova. Learn more on Medium

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre Extração de Features de Texto e Imagem Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre pipelines e composições de estimadores. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre pipelines e composições de estimadores. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre partial dependence plots e permutation importances. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre persistência de modelos e curvas de aprendizado e validação. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre tuning de modelos e métricas. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre validação cruzada. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre detecção de anomalias, estimação de matrizes de covariâncias, estimação de densidade e Restricted Boltzmann Machines. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre Biclustering e Fatoração de Matrizes. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre Clustering. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre Manifold Learning. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre Redes Neurais e Gaussian Mixture Models, Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre calibragem da probabilidade dos modelos. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre as ferramentas para lidar com problemas multioutput e multilabel. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre ensembles. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre árvores de decisão, a base dos meus modelos favoritos. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

In this episode, I talk about XGBoost 1.0, a major milestone for this very popular algorithm. Then, I discuss the three options you have for running XGBoost on Amazon SageMaker: built-in algo, built-in framework, and bring your own container. Code included, of course!⭐️⭐️⭐️ Don't forget to subscribe to be notified of future episodes ⭐️⭐️⭐️Additional resources mentioned in the podcast:* XGBoost built-in algo: https://gitlab.com/juliensimon/ent321* XGBoost built-in framework: https://gitlab.com/juliensimon/dlnotebooks/-/blob/master/sagemaker/09-XGBoost-script-mode.ipynb * BYO with Scikit-learn: https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/scikit_bring_your_own/scikit_bring_your_own.ipynb * Deploying XGBoost with mlflow: https://youtu.be/jpZSp9O8_ew * New model format: https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html* Converting pickled models: https://github.com/dmlc/xgboost/blob/master/doc/python/convert_090to100.py This podcast is also available in video at https://youtu.be/w0F4z0dMdzI.For more content, follow me on:* Medium https://medium.com/@julsimon* Twitter https://twitter.com/@julsimon

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre Gaussian Processes, Naive Bayes e uma reflexão sobre como ensinamos machine learning. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre SGD e Nearest Neighbors. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre SVM, Truque do Kernel e Discriminant Analysis no segundo dia do desafio dos mestres do scikit-learn. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre modelos lineares no primeiro dia do desafio dos mestres do scikit-learn. [TREINAMENTO] Os 3 Segredos De Um Campeão Do Kaggle Para Criar Modelos De Machine Learning Que Vencem Competições:webinar.mariofilho.com

When speaking about Bayesian statistics, we often hear about « probabilistic programming » — but what is it? Which languages and libraries allow you to program probabilistically? When is Stan, PyMC, Pyro or any other probabilistic programming language most appropriate for your project? And when should you even use Bayesian libraries instead of non-bayesian tools, like Statsmodels or Scikit-learn? Colin Carroll will answer all these questions for you. Colin is a machine learning researcher and software engineer who’s notably worked on modeling risk in the airline industry and building NLP-powered search infrastructure for finance. He’s also an active contributor to open source, particularly to the popular PyMC3 and ArviZ libraries. Having studied geometric measure theory at Rice University, Colin was bound to walk in the woods with Pete the pup – who was there when we recorded by the way – and to launch balloons into near-space in his spare time. Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/! Links from the show: Colin's blog: https://colindcarroll.com/ Colin on Twitter: https://twitter.com/colindcarroll Colin on GitHub: https://github.com/ColCarroll Very parallel MCMC sampling: https://colindcarroll.com/2019/08/18/very-parallel-mcmc-sampling/ A tour of probabilistic programming APIs: https://colindcarroll.com/2019/07/23/a-tour-of-probabilistic-programming-apis/ PyMC3, Probabilistic Programming in Python: https://docs.pymc.io/ Stan: https://mc-stan.org/ Pyro, Deep Universal Probabilistic Programming: https://pyro.ai/ ArviZ, Exploratory analysis of Bayesian models: https://arviz-devs.github.io/arviz/ PyMC-Learn, Probabilistic models for machine learning: https://www.pymc-learn.org/ Facebook’s Prophet uses Stan: https://statmodeling.stat.columbia.edu/2017/03/01/facebooks-prophet-uses-stan/ Prophet in PyMC3: https://github.com/luke14free/pm-prophet --- Send in a voice message: https://anchor.fm/learn-bayes-stats/message

Today we're joined by Paul Mahler, senior data scientist and technical product manager for machine learning at NVIDIA. In our conversation, Paul and I discuss NVIDIA's RAPIDS open source project, which aims to bring GPU acceleration to traditional data science workflows and machine learning tasks. We dig into the various subprojects like cuDF and cuML that make up the RAPIDS ecosystem, as well as the role of lower-level libraries like mlprims and the relationship to other open-source projects like Scikit-learn, XGBoost and Dask. The complete show notes for this episode can be found at https://twimlai.com/talk/254. Visit twimlai.com/gtc19 for more from our GTC 2019 series. To learn more about Dell Precision workstations, and some of the ways they’re being used by customers in industries like Media and Entertainment, Engineering and Manufacturing, Healthcare and Life Sciences, Oil and Gas, and Financial services, visit Dellemc.com/Precision.

What does it take to become a data scientist? We speak with three people who have become data scientists in the last three years and find out what it takes, in their opinions, to land a data science job and to be prepared for a career in the field. Curtis: We’ve talked a lot in our recent episodes about all the interesting things you can do with data science, and we’ve only talked a little bit recently about what it actually takes to get into the field, which is a topic that a lot of you have reached out to us and asked us to cover in a more thorough way. So today, we’re taking a broader approach on this topic by talking to three data scientists who have become data scientists in the last three years. You’re going to be able to hear all the details of each of their three journeys, how they got started, how they landed their jobs, and what their best advice is for getting into the field, and this will give you a broad view about how to get into data science from three people who have actually done it. Ginette: I’m Ginette. Curtis: And I’m Curtis. Ginette: And you are listening to Data Crunch. Curtis: A podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Ginette: A Vault Analytics production. Ginette: Here at Data Crunch we’ve been hard at work developing a technology that allows executives and business leaders to gain insight from their data instantly—simply by talking to the air. We hook up your data to an Alexa device with custom skills built in to understand the questions you have about your business - and give you answers. Figure out sales forecasts, marketing performance, operational compliance, progress on KPIs, and more by just talking to Alexa. We are officially launching the product this week and have room for three initial customers—if you're interested, head over to datacrunchcorp.com/alexa or datacrunchpodcast.com/alexa (both work), and book some time to chat with us. We’ll assess if your company is a good fit, and if so, we look forward to working with you! Tyler Folkman: My name’s Tyler Folkman. I've gotten into data science in kind of a strange route to be honest. I did my undergrad in economics, actually originally thinking to get into computer science, but for some reason, I had this thought that computer science was going to get outsourced; I don't know if that was a thing, but I think people back in the early 2000s were talking about computer science getting outsourced, so I thought about business, which ended up begin economics, which I really liked, and then ended up doing economic consulting, which is, basically in usually large litigation cases, lawyers hire economists to value damages, so for example, when Samsung and Apple were suing each other, I worked on the Samsung side to help value how much they might sue Apple for, for patent infringement, and a lot of that involves statistical analyses, data analytics, econometrics as economists would call it. And I got really interested in just this idea of data being a really powerful tool for making decisions and coming to conclusions, and so I started hearing about machine learning on the Internet, kind of dabbling with Python, which at the time, I was a Windows user, and it was a huge pain to get Python installed, but I kind of got it up and running, played around with things like SciKit learn, read some blogs, and really got into machine learning and found that it was really housed more in the computer science department at that time, and just kind of decided to apply to some computer science departments and was lucky to get in at University of Texas at Austin and do some studies there, join a machine learning lab and got to do some work at Amazon. Really got a really good set of experiences to kind of help me learn how to be both a programmer and a machine learning person, a little bit of statistics, and jumped straight from there over here to Ancestry and was luc...

كملنا كلامنا في هذه الحلقة مع ضيفنا رامي عن الذكاء الإصطناعي عن طريق المحاور التالية: - تلخيص الحلقة الأولى - مناقشة بعض ردود الفعل من مستمعينا - مفاهيم خاطئة حول الذكاء الاصطناعي - المحفزات للعمل على الذكاء الاصطناعي - أدوات للمطورين الراغبين في دخول المجال روابط الأدوات وما تم مناقشته: Python https://www.python.org/ Scikit-learn http://scikit-learn.org/ Tensor flow https://www.tensorflow.org/ الموقع الي تم تجربته لشرح الشبكة: http://playground.tensorflow.org/ Google NPL https://cloud.google.com/natural-language/ الكود على صفحة رامي على Github: https://github.com/alshafi/Learning_machine_learning -------------------------------------------------------- خالد العريفي https://twitter.com/AboMayar رامي الشافي https://twitter.com/Dr_rami

Tweet this Episode Tyler Renelle is a contractor and developer who has worked in various web technologies like Node, Angular, Rails, and much more. He's also build machine learning backends in Python (Flask), Tensorflow, and Neural Networks. The JavaScript Jabber panel dives into Machine Learning with Tyler Renelle. Specifically, they go into what is emerging in machine learning and artificial intelligence and what that means for programmers and programming jobs. This episode dives into: Whether machine learning will replace programming jobs Economic automation Which platforms and languages to use to get into machine learning and much, much more... Links: Raspberry Pi Arduino Hacker News Neural Networks (wikipedia) Deep Mind Shallow Algorithms Genetic Algorithms Crisper gene editing Wix thegrid.io Codeschool Codecademy Tensorflow Keras Machine Learning Guide Andrew Ng Coursera Course Python R Java Torch PyTorch Caffe Scikit learn Tensorfire DeepLearn.js The Singularity is Near by Ray Kurzweil Tensorforce Super Intelligence by Nick Bostrom Picks: Aimee Include media Nodevember Phone cases AJ Data Skeptic Ready Player One Joe Everybody Lies Tyler Ex Machina Philosophy of Mind: Brains, Consciousness, and Thinking Machines

Tweet this Episode Tyler Renelle is a contractor and developer who has worked in various web technologies like Node, Angular, Rails, and much more. He's also build machine learning backends in Python (Flask), Tensorflow, and Neural Networks. The JavaScript Jabber panel dives into Machine Learning with Tyler Renelle. Specifically, they go into what is emerging in machine learning and artificial intelligence and what that means for programmers and programming jobs. This episode dives into: Whether machine learning will replace programming jobs Economic automation Which platforms and languages to use to get into machine learning and much, much more... Links: Raspberry Pi Arduino Hacker News Neural Networks (wikipedia) Deep Mind Shallow Algorithms Genetic Algorithms Crisper gene editing Wix thegrid.io Codeschool Codecademy Tensorflow Keras Machine Learning Guide Andrew Ng Coursera Course Python R Java Torch PyTorch Caffe Scikit learn Tensorfire DeepLearn.js The Singularity is Near by Ray Kurzweil Tensorforce Super Intelligence by Nick Bostrom Picks: Aimee Include media Nodevember Phone cases AJ Data Skeptic Ready Player One Joe Everybody Lies Tyler Ex Machina Philosophy of Mind: Brains, Consciousness, and Thinking Machines

Tweet this Episode Tyler Renelle is a contractor and developer who has worked in various web technologies like Node, Angular, Rails, and much more. He's also build machine learning backends in Python (Flask), Tensorflow, and Neural Networks. The JavaScript Jabber panel dives into Machine Learning with Tyler Renelle. Specifically, they go into what is emerging in machine learning and artificial intelligence and what that means for programmers and programming jobs. This episode dives into: Whether machine learning will replace programming jobs Economic automation Which platforms and languages to use to get into machine learning and much, much more... Links: Raspberry Pi Arduino Hacker News Neural Networks (wikipedia) Deep Mind Shallow Algorithms Genetic Algorithms Crisper gene editing Wix thegrid.io Codeschool Codecademy Tensorflow Keras Machine Learning Guide Andrew Ng Coursera Course Python R Java Torch PyTorch Caffe Scikit learn Tensorfire DeepLearn.js The Singularity is Near by Ray Kurzweil Tensorforce Super Intelligence by Nick Bostrom Picks: Aimee Include media Nodevember Phone cases AJ Data Skeptic Ready Player One Joe Everybody Lies Tyler Ex Machina Philosophy of Mind: Brains, Consciousness, and Thinking Machines

Computer vision is a complex field that spans industries with varying needs and implementations. Scikit-Image is a library that provides tools and techniques for people working in the sciences to process the visual data that is critical to their research. This week Stefan Van der Walt and Juan Nunez-Iglesias, co-authors of Elegant SciPy, talk about how the project got started, how it works, and how they are using it to power their experiments.

Scikit-learn is a set of machine learning tools in Python that provides easy-to-use interfaces for building predictive models. In a previous episode with Per Harald Borgen about Machine Learning For Sales, he illustrated how easy it is to get up and running and productive with scikit-learn, even if you are not a machine learning expert. The post Scikit-learn with Andreas Mueller appeared first on Software Engineering Daily.

We're excited to welcome a guest, Tim Head, who is one of the maintainers of the scikit-optimize package. With all the talk about optimization lately, it felt appropriate to get in a few words with someone who's out there making it happen for python. Relevant links: https://scikit-optimize.github.io/ http://www.wildtreetech.com/