Podcasts about scikit learn

"C'est le travail sur ces sujets qui a permis toute ces avancées des LLMs"Le D.E.V. de la semaine est Vincent Maladiere, co-founder @ Probabl. Vincent et Bruno explorent le rôle des modèles prédictifs dans l'analyse des données et leur aptitude à dévoiler la causalité, avec un focus sur l'intelligence artificielle. Ils discutent également des avancées dans Scikit-Learn et de la nécessité de repérer les liens causaux dépassant une simple corrélation. Certains défis évoqués sont le biais de sélection, l'analyse de survie et les complications liées aux données manquantes. Le duo conclut en réitérant que, malgré les progrès des modèles de langage, ils devraient compléter, plutôt que remplacer, les méthodes traditionnelles. Vincent termine en soulignant l'importance de l'apprentissage continu.Chapitrages00:00:53 : Introduction à la Prédiction et Causalité00:01:34 : Présentation de Vincent00:03:16 : Concepts Mathématiques et Philosophiques00:03:27 : Découverte de Scikit-Learn00:06:38 : Évolution de Scikit-Learn vers l'Industrie00:07:45 : Signaux, Causalité et Modèles00:11:30 : Prédiction vs Causalité00:15:48 : Exemples de Causalité dans Divers Domaines00:20:11 : Évaluation de Tests A/B et Causalité00:28:52 : Automatisation des Décisions Basées sur des Modèles00:30:53 : Projet ROAS et Prédiction00:33:11 : Importance des Cohortes dans l'Analyse00:35:03 : La Notion de Survie en Statistiques00:43:12 : Feature Engineering et Causalité00:47:36 : L'Impact des LLM sur la Prédiction00:52:53 : Mélange de Techniques Traditionnelles et Modernes00:55:44 : Recommandations de Contenus00:56:29 : Conclusion et Remerciements Liens évoqués pendant l'émissionSilicon Valley**Recrutez les meilleurs développeurs grâce à Indeed !** "Trouver des développeurs compétents et passionnés, comme les auditeurs d'If This Then Dev, peut être un vrai défi. Avec Indeed, connectez-vous rapidement avec des candidats qualifiés qui sauront s'épanouir dans votre entreprise. Profitez dès maintenant d'un crédit de 100 euros pour sponsoriser votre offre d'emploi : go.indeed.com/IFTTD."🎙️ Soutenez le podcast If This Then Dev ! 🎙️ Chaque contribution aide à maintenir et améliorer nos épisodes. Cliquez ici pour nous soutenir sur Tipeee 🙏Archives | Site | Boutique | TikTok | Discord | Twitter | LinkedIn | Instagram | Youtube | Twitch | Job Board |Distribué par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

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

Recently the company stewarding the open source library scikit-learn announced their seed funding. Also, OpenAI released "o1" with new behavior in which it pauses to "think" about complex tasks. Chris and Daniel take some time to do their own thinking about o1 and the contrast to the scikit-learn ecosystem, which has the goal to promote "data science that you own."

Recently the company stewarding the open source library scikit-learn announced their seed funding. Also, OpenAI released "o1" with new behavior in which it pauses to "think" about complex tasks. Chris and Daniel take some time to do their own thinking about o1 and the contrast to the scikit-learn ecosystem, which has the goal to promote "data science that you own."

In this podcast episode, we talked with Guillaume Lemaître about navigating scikit-learn and imbalanced-learn.

Historically it's always been the case that you would use a pickle file to store a trained scikit-learn model on disk for deployment. Pickles make sense because these are so flexible, but they do carry a security concern. Adrin has been working on a remedy called skops, which is the main topic of this podcast. To learn more about skops, make sure to check the documentation: https://skops.readthedocs.io/en/stable/

#179: Nicholas Ciufentes-Goodbody transitioned from a career in teaching to becoming the Chief Data Scientist at WorldQuant University.  He explains what data science involves, what a career in data science looks like, and why it's such a popular field to work in. What you'll learn[1:45] What WorldQuant University is and the types of programs they run. [3:42] How a free university is possible in America.[4:13] The motivation for a hedge fund to run a free university. [4:50] What data science is and how it's used. [6:03] The biggest employers of data scientists. [8:21] What a typical day as a data scientist is like. [10:12] The different specialities within data science. [12:08] What it means to be an AI engineer. [12:40] What qualifications you need to become a data scientist. [14:43] The level of education you need to become a data scientist. [16:27] Character traits that successful data scientists share. [18:10] The amount of nerds working in data science. [20:08] Why musicians become data scientists and doctors. [21:15] How to transition your career to become a data scientist. [27:37] What you need on your resume when applying for a data science role. [29:07] The best ways to learn the skills you need to become a data scientist.  [30:29] How to edit your CV when applying for a data science job. [31:55] How to find a good data science boot camp. [33:51] The base knowledge you need prior to joining a data science boot camp. [36:30] The income potential of a data scientist. [38:17] The career path of a data scientist. [39:28] The impact AI will have on data scientists. [42:12] The ever-changing nature of data science. Resources mentioned in this episodePlease note that some of these are affiliate links and we may get a commission in the event that you make a purchase.  This helps us to cover our expenses and is at no additional cost to you.DataCampKaggleopenAFRICACourseraUdemyAn Introduction to Statistical Learning, Gareth JamesHands-on machine learning with Scikit-Learn, Keras, and TensorFlow, Aurelien GeronFluent Python, Luciano RamalhoMathematics for Human Flourishing, Francis SuFor the show notes for this episode, including a full transcript and links to all the resources mentioned, visit:https://changeworklife.com/using-chatgpt-to-supercharge-your-career/Re-assessing your career?  Know you need a change but don't really know where to start?  Check out these two exercises to start the journey of working out what career is right for you!

O natalense Bernardo se mudou para Brasília aos 8 anos de idade e, após se formar em Engenharia Elétrica com Especialização em Telecomunicações, passou a trabalhar na área, no negócio da família. Depois de constituir a própria família, Bernardo passou a pensar em morar no exterior, em busca de uma maior sensação de segurança. Como já possuía raízes e conhecidos em Portugal, mudou-se para lá onde, com seu background de engenharia (e aconselhado pelo hoje co-apresentador do podcast Let's Data, Leon Sólon), passou a estudar Ciência de Dados. Hoje, além de manter o podcast e a iniciativa Let's Data, Bernardo trabalha como cientista de dados na NielsenIQ e nos conta sobre a necessidade de se ter uma educação formal para buscar trabalho, além das particularidades – boas e talvez não tão boas – de se morar na terra do pastel de Belém nata. Fabrício Carraro, o seu viajante poliglota Bernardo Lago, Cientista de Dados Sênior em Lisboa, Portugal Links: Let's Data Podcast Let's Data Instagram do Bernardo Conheça a Escola de Data Science da Alura, descubra as diferentes possibilidades de análise de dados, do Excel ao Python, e mergulhe em frameworks e bibliotecas, como Pandas, Scikit-Learn e Seaborn. TechGuide.sh, um mapeamento das principais tecnologias demandadas pelo mercado para diferentes carreiras, com nossas sugestões e opiniões. #7DaysOfCode: Coloque em prática os seus conhecimentos de programação em desafios diários e gratuitos. Acesse https://7daysofcode.io/ Ouvintes do podcast Dev Sem Fronteiras têm 10% de desconto em todos os planos da Alura Língua. Basta ir a https://www.aluralingua.com.br/promocao/devsemfronteiras/e começar a aprender inglês e espanhol hoje mesmo!  Produção e conteúdo: Alura Língua Cursos online de Idiomas – https://www.aluralingua.com.br/ Alura Cursos online de Tecnologia – https://www.alura.com.br/ Edição e sonorização: Rede Gigahertz de Podcasts

scikit-learn is a highly successful and popular Python library for data science and machine learning. It is open source and has a large contributor base. I had the pleasure to meet with some of the scikit-learn team to talk about how they got involved and how it is possible to run an Open Source project of this size and scale. If you like to get involved, here are a few links to their home page and GitHub repository.https://scikit-learn.org/stable/index.html scikit-learn homepagehttps://github.com/scikit-learn/scikit-learn GitHub repohttps://blog.scikit-learn.org Blog postGo to the community page of scikit-learn to get links to LinkedIn, Twitter and othersSupport 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/

Probabl isn't your average AI startup as this new French company is an Inria spin-off company that revolves around an open-source data science library called scikit-learn Learn more about your ad choices. Visit megaphone.fm/adchoices

scikit-learn co-founder Gaël Varoquaux and Jon Krohn are live at the historic Sorbonne in Paris, where they discuss the evolution of scikit-learn. From its origins as a memory-efficient Python implementation of support vector machines to its present-day status as a pivotal resource in machine learning, Gaël paints a vivid picture of its remarkable growth. Join us for a glimpse into scikit-learn's evolution, the realm of open-source collaboration, and the transformative power of data-driven insights in today's dynamic data landscape. This episode is brought to you by Gurobi (gurobi.com/sds), the Decision Intelligence Leader, by Data Universe (https://datauniverse2024.com), the out-of-this-world data conference, and by CloudWolf (www.cloudwolf.com/sds), the Cloud Skills platform. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information. In this episode you will learn: • The early beginnings and growth of scikit-learn [05:34] • Development principles of scikit-learn [18:05] • How to apply scikit-learn to your ML problem [21:16] • Resource-efficiency and scikit-learn development [25:32] • How to contribute to an open-source project like scikit-learn yourself [38:21] • The future of scikit-learn [51:13] • Gaël on the social-impact data projects in his Soda lab [1:02:33] • Why domain expertise and statistical rigor are more important than ever [1:11:24] Additional materials: www.superdatascience.com/737

In this episode of Elixir Wizards, Katelynn Burns, software engineer at LaunchScout, and Alexis Carpenter, senior data scientist at cars.com, join Host Dan Ivovich to discuss machine learning with Elixir, Python, SQL, and MATLAB. They compare notes on available tools, preprocessing, working with pre-trained models, and training models for specific jobs. The discussion inspires collaboration and learning across communities while revealing the foundational aspects of ML, such as understanding data and asking the right questions to solve problems effectively. Topics discussed: Using pre-trained models in Bumblebee for Elixir projects Training models using Python and SQL The importance of data preprocessing before building models Popular tools used for machine learning in different languages Getting started with ML by picking a personal project topic of interest Resources for ML aspirants, such as online courses, tutorials, and books The potential for Elixir to train more customized models in the future Similarities between ML approaches in different languages Collaboration opportunities across programming communities Choosing the right ML approach for the problem you're trying to solve Productionalizing models like fine-tuned LLM's The need for hands-on practice for learning ML skills Continued maturation of tools like Bumblebee in Elixir Katelynn's upcoming CodeBeam talk on advanced motion tracking Links mentioned in this episode https://launchscout.com/ https://www.cars.com/ Genetic Algorithms in Elixir (https://pragprog.com/titles/smgaelixir/genetic-algorithms-in-elixir/) by Sean Moriarity Machine Learning in Elixir (https://pragprog.com/titles/smelixir/machine-learning-in-elixir/) by Sean Moriarity https://github.com/elixir-nx/bumblebee https://github.com/huggingface https://www.docker.com/products/docker-hub/ Programming with MATLAB (https://www.mathworks.com/products/matlab/programming-with-matlab.html) https://elixirforum.com/ https://pypi.org/project/pyspark/  Machine Learning Course (https://online.stanford.edu/courses/cs229-machine-learning) from Stanford School of Engineering Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) by Aurélien Géron Data Science for Business (https://data-science-for-biz.com/) by Foster Provost & Tom Fawcett https://medium.com/@carscomtech  https://github.com/k-burns  Code Beam America (https://codebeamamerica.com/) March, 2024 Special Guests: Alexis Carpenter and Katelynn Burns.

⬇⬇⬇APRIMI⬇⬇ Abbonati qui: https://www.youtube.com/economiaitalia/join https://www.patreon.com/join/EconomiaItalia? LO SPORCO SEGRETO di scikit-learn che probabilmente ha fatto perdere MILIONI a svariate compagnie In un mondo di modelli predittivi, la calibrazione delle probabilità è essenziale. Immagina di vendere due tazze diverse e dover prevedere quale un cliente comprerebbe. La "regressione isotonica" può migliorare la precisione, allineando le probabilità stimate con quelle reali. Una calibrazione accurata è cruciale per decisioni commerciali sagge. Qui per segnalare temi: https://tellonym.me/dr.elegantia Podcast (su tutte le piattaforme): https://www.spreaker.com/show/dr-elegantia-podcast COME SOSTENERCI: Il nostro nuovo libro sull'economia: Guida Terrestre per Autoeconomisti https://www.poliniani.com/product-page/guida-terrestre link acquisto Amazon: https://amzn.to/36XTXs8 Acquistando le nostre T-shirt dedicate ai dati stampate in Serigrafia Artigianale con passione e orgoglio dai detenuti del Carcere Lorusso e Cutugno di Torino https://bit.ly/3zNsdkd e HTTPS://urly.it/3nga1 Guida al VOTO 2022: https://amzn.to/3KflXHd DonazionI Paypal: https://paypal.me/appuntiUAB Vuoi sostenermi ma non sborsare nemmeno un euro? Usa questo link per per il tuo prossimo acquisto su Amazon: https://amzn.to/2JGRyGT Qui trovi i libri che consiglio per iniziare a capirne di più sull'economia: https://www.youtube.com/watch?v=uEaIk8wQ3z8 Dove ci trovi: https://www.umbertobertonelli.it/info/ https://linktr.ee/economiaitalia La mia postazione: Logitech streamcam https://amzn.to/3HR6xq0 Luci https://amzn.to/3n6qtgP Shure MV7https://amzn.to/3HRh7k1 Asta https://amzn.to/3HSRvzY #economiaitalia #drelegantia #economia

⬇⬇⬇APRIMI⬇⬇ Abbonati qui: https://www.youtube.com/economiaitalia/join https://www.patreon.com/join/EconomiaItalia? LO SPORCO SEGRETO di scikit-learn che probabilmente ha fatto perdere MILIONI a svariate compagnie In un mondo di modelli predittivi, la calibrazione delle probabilità è essenziale. Immagina di vendere due tazze diverse e dover prevedere quale un cliente comprerebbe. La "regressione isotonica" può migliorare la precisione, allineando le probabilità stimate con quelle reali. Una calibrazione accurata è cruciale per decisioni commerciali sagge. Qui per segnalare temi: https://tellonym.me/dr.elegantia Podcast (su tutte le piattaforme): https://www.spreaker.com/show/dr-elegantia-podcast COME SOSTENERCI: Il nostro nuovo libro sull'economia: Guida Terrestre per Autoeconomisti https://www.poliniani.com/product-page/guida-terrestre link acquisto Amazon: https://amzn.to/36XTXs8 Acquistando le nostre T-shirt dedicate ai dati stampate in Serigrafia Artigianale con passione e orgoglio dai detenuti del Carcere Lorusso e Cutugno di Torino https://bit.ly/3zNsdkd e HTTPS://urly.it/3nga1 Guida al VOTO 2022: https://amzn.to/3KflXHd DonazionI Paypal: https://paypal.me/appuntiUAB Vuoi sostenermi ma non sborsare nemmeno un euro? Usa questo link per per il tuo prossimo acquisto su Amazon: https://amzn.to/2JGRyGT Qui trovi i libri che consiglio per iniziare a capirne di più sull'economia: https://www.youtube.com/watch?v=uEaIk8wQ3z8 Dove ci trovi: https://www.umbertobertonelli.it/info/ https://linktr.ee/economiaitalia La mia postazione: Logitech streamcam https://amzn.to/3HR6xq0 Luci https://amzn.to/3n6qtgP Shure MV7https://amzn.to/3HRh7k1 Asta https://amzn.to/3HSRvzY #economiaitalia #drelegantia #economiaDiventa un supporter di questo podcast: https://www.spreaker.com/podcast/dr-elegantia-podcast--5692498/support.

In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Machine Learning Tools: Keras, PyTorch, Scikit Learn, TensorFlow, Apache Spark, Kaggle, explain how these terms relate to AI and why it's important to know about them. Show Notes: FREE Intro to CPMAI mini course CPMAI Training and Certification AI Glossary Glossary Series: (Artificial) Neural Networks, Node (Neuron), Layer Glossary Series: Bias, Weight, Activation Function, Convergence, ReLU Glossary Series: Perceptron Glossary Series: Hidden Layer, Deep Learning Glossary Series: Loss Function, Cost Function & Gradient Descent Glossary Series: Backpropagation, Learning Rate, Optimizer Glossary Series: Feed-Forward Neural Network AI Glossary Series – Machine Learning, Algorithm, Model Continue reading AI Today Podcast: AI Glossary Series – Machine Learning Tools: Keras, PyTorch, Scikit Learn, TensorFlow, Apache Spark, Kaggle at Cognilytica.

Alex O'Connor—researcher and ML manager—on the latest trends of generative AI. Language and image models, prompt engineering, the latent space, fine-tuning, tokenization, textual inversion, adversarial attacks, and more. Alex O'Connor got his PhD in Computer Science from Trinity College, Dublin. He was a postdoctoral researcher and funded investigator for the ADAPT Centre for digital content, at both TCD and later DCU. In 2017, he joined Pivotus, a Fintech startup, as Director of Research. Alex has been Sr Manager for Data Science & Machine Learning at Autodesk for the past few years, leading a team that delivers machine learning for e-commerce, including personalization and natural language processing. Favorite quotes “None of these models can read.” “Art in the future may not be good, but it will be prompt.” Mastodon Books Machine Learning Systems Design by Chip Huyen Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron Papers The Illustrated Transformer by Jay Alammar Attention Is All You Need by Google Brain Transformers: a Primer by Justin Seonyong Lee Links Alex in Mastodon ★ Training Dream Booth Multimodal Art on HuggingFace by @akhaliq NeurIPS arxiv.org: Where most papers get published Nono's Discord Suggestive Drawing: Nono's master's thesis Crungus is a fictional character from Stable Diffusion's latent space Machine learning models Stable Diffusion Arcane Style Stable Diffusion fine-tuned model ★ Imagen DALL-E CLIP GPT and ChatGPT BERT, ALBERT & RoBERTa Bloom word2vec Mupert.ai and Google's MusicLM t-SNE and UMAP: Dimensionality reduction techniques char-rnn Sites TensorFlow Hub HuggingFace Spaces ★ DreamBooth Jasper AI Midjourney Distill.pub ★ Concepts High-performance computing (HPC) Transformers and Attention Sequence transformers Quadratic growth Super resolution Recurrent neural networks (RNNs) Long short-term memory networks (LSTMs) Gated recurrent units (GRUs) Bayesian classifiers Machine translation Encoder-decoder Gradio Tokenization ★ Embeddings ★ Latent space The distributional hypothesis Textual inversion ★ Pretrained models Zero-shot learning Mercator projection People mentioned Ted Underwood UIUC Chip Huyen Aurélien Géron Chapters 00:00 · Introduction 00:40 · Machine learning 02:36 · Spam and scams 15:57 · Adversarial attacks 20:50 · Deep learning revolution 23:06 · Transformers 31:23 · Language models 37:09 · Zero-shot learning 42:16 · Prompt engineering 43:45 · Training costs and hardware 47:56 · Open contributions 51:26 · BERT and Stable Diffusion 54:42 · Tokenization 59:36 · Latent space 01:05:33 · Ethics 01:10:39 · Fine-tuning and pretrained models 01:18:43 · Textual inversion 01:22:46 · Dimensionality reduction 01:25:21 · Mission 01:27:34 · Advice for beginners 01:30:15 · Books and papers 01:34:17 · The lab notebook 01:44:57 · Thanks I'd love to hear from you. Submit a question about this or any previous episodes. Join the Discord community. Meet other curious minds. If you enjoy the show, would you please consider leaving a short review on Apple Podcasts/iTunes? It takes less than 60 seconds and really helps. Show notes, transcripts, and past episodes at gettingsimple.com/podcast. Thanks to Andrea Villalón Paredes for editing this interview. Sleep and A Loop to Kill For songs by Steve Combs under CC BY 4.0. Follow Nono Twitter.com/nonoesp Instagram.com/nonoesp Facebook.com/nonomartinezalonso YouTube.com/nonomartinezalonso

A entrevistada, Carla Florida, desse episódio trás experiências como pesquisadora de inteligência artifical na ICI UPB (universidade) da Bolívia. Vem assistir e dizer pra gente o que tu achou. Apoie o pizza: Se você ou sua empresa deseja apadrinhar episódios do Pizza e contribuir com a disseminação do conteúdo de ciência de dados em português, manda um e-mail pra gente. --------   Participantes: Carla Florida Instagram Ana Cecília Vieira Twitter Escute agora: Recomendação de leitura: Mãos à Obra: Aprendizado de Máquina com Scikit-Learn e TensorFlow (Hands–On Machine Learning with Scikit–Learn and TensorFlow) -------- Créditos: Produção Esse episódio foi produzido com a colaboração de Ana Cecília Vieira. -------- Escute:

This episode we welcome Sebastian Raschka, Lead AI Educator at Lightning and author of Machine Learning with Pytorch and Scikit-Learn to discuss the best ways to learn machine learning, his open source work, how to use chatGPT, AGI, responsible AI and so much more. Sebastian is a fountain of knowledge and it was a pleasure to get his insights on this fast moving industry. Learning from Machine Learning, a podcast that explores more than just algorithms and data: Life lessons from the experts. Resources to learn more about Sebastian Raschka and his work:https://sebastianraschka.com/https://lightning.ai/Machine Learning with Pytorch and Scikit-LearnMachine Learning Q and AIResources to learn more about Learning from Machine Learning and the host: https://www.linkedin.com/company/learning-from-machine-learninghttps://www.linkedin.com/in/sethplevine/https://medium.com/@levine.seth.ptwitterReferences from Episodehttps://scikit-learn.org/stable/http://rasbt.github.io/mlxtend/https://github.com/BioPandas/biopandasUnderstanding and Coding the Self-Attention Mechanism of Large Language Models From ScratchAndrew Ng - https://www.andrewng.org/Andrej Karpathy - https://karpathy.ai/Paige Bailey - https://github.com/dynamicwebpaigeContents01:15 - Career Background05:18 - Industry vs. Academia08:18 - First Project in ML15:04 - Open Source Projects Involvement20:00 - Machine Learning: Q&AI24:18 - ChatGPT as Brainstorm Assistant25:38 - Hype vs. Reality27:55 - AGI31:00 - Use Cases for Generative Models34:01 - Should the goal to be to replicate human intelligence?39:18 - Delegating Tasks using LLM42:26 - ML Models are overconfident on Out of Distribution44:54 - Responsible AI and ML45:59 - Complexity of ML Systems47:26 - Trend for ML Practitioners to move to AI Ethics49:27 - What advice would you give to someone just starting out?52:20 - Advice that you've received that has helped you54:08 - Andrew Ng Advice55:20 - Exercise of Implementing Algorithms from Scratch59:00 - Who else has influenced you?01:01:18 - Production and Real-World Applications - Don't reinvent the wheel01:03:00 - What has a career in ML taught you about life?

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

Sebastian Raschka​ is the lead AI educator at GridAI. He is the author of the book "Machine Learning with PyTorch and Scikit Learn" and also a few other books that cover the fundamentals of #machinelearning and #deeplearning techniques and implementing them with Python. He is also an Assistant Professor of Statistics at the University of Wisconsin-Madison and has been actively involved in making ML more accessible to beginners through his blogs, video tutorials, tweets and of course his books. He also holds a doctorate in Computational and Quantitative Biology from Michigan State University.Time Stamps of the Podcast00:00:00 Introductions00:02:40 Entry point in AI/ML that made you interested in it00:05:30 How did you go about learning the basics and implementation of various methods?00:11:45 What makes Python ideal for learning Machine Learning recently?00:21:54 What is your book about and who is this for?00:33:55 What goes into writing a good technical book?00:40:50 Applying ML to toy datasets vs real-world research problems00:47:40 Choosing b/w machine learning methods & deep learning methods00:56:22 Large models vs architecture efficient models 01:01:25 Interpretability & Explainability in AI01:08:45 Insights for people interested in machine learning research, academia or PhD01:14:17 Keeping up with research in deep learningSebastian's homepage: https://sebastianraschka.com/Twitter: https://mobile.twitter.com/rasbtLinkedIn: https://www.linkedin.com/in/sebastianraschka/His book: https://www.amazon.com/Machine-Learning-PyTorch-Scikit-Learn-scikit-learn-ebook-dp-B09NW48MR1/dp/B09NW48MR1/Video Tutorials:  @SebastianRaschka  About the Host:Jay is a Ph.D. student at Arizona State University.Linkedin: https://www.linkedin.com/in/shahjay22/Twitter: https://twitter.com/jaygshah22Reach out to https://www.public.asu.edu/~jgshah1/ for any queries.Stay tuned for upcoming webinars!***Disclaimer: The information contained in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.***Checkout these Podcasts on YouTube: https://www.youtube.com/c/JayShahmlAbout the author: https://www.public.asu.edu/~jgshah1/

Emmanuel Turlay spent more than a decade in engineering roles at tech-first companies like Instacart and Cruise before realizing machine learning engineers need a better solution. Emmanuel started Sematic earlier this year and was part of the YC summer 2022 batch. He recently raised a $3M seed round from investors including Race Capital and Soma Capital. Thanks to friend of the podcast and former guest Hina Dixit from Samsung NEXT for the intro to Emmanuel.I've been involved with the AutoML space for five years and, for full disclosure, I'm on the board of Auger which is in a related space. I've seen the space evolve and know how much room there is for innovation. This one's a great education about what's broken and what's ahead from a true machine learning pioneer.Listen and learn...How to turn every software engineer into a machine learning engineerHow AutoML platforms are automating tasks performed in traditional ML toolsHow Emmanuel translated learning from Cruise, the self-driving car company, into an open source platform available to all data engineering teamsHow to move from building an ML model locally to deploying it to the cloud and creating a data pipeline... in hoursWhat you should know about self-driving cars... from one of the experts who developed the brains that power themWhy 80% of AI and ML projects failReferences in this episode:Unscrupulous users manipulate LLMs to spew hateHina Dixit from Samsung NEXT on AI and the Future of WorkApache BeamEliot Shmukler, Anomalo CEO, on AI and the Future of Work

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. 

This interview was recorded for GOTO Unscripted.gotopia.techRead the full transcription of this interview hereOscar Beijbom - Co-Founder at NyckelPrayson Daniel - Principal Data Scientist at NTT DATADESCRIPTIONSelf-driving vehicles have been a hot topic for a while now and everyone is waiting for the next breakthrough. Prayson Daniel, principal data scientist at NTT DATA, and Oscar Beijbom, co-founder at Nyckel, stuck their heads together to review what type of machine learning data is needed to run an autonomous vehicle. Furthermore, they talked about topics such as security, language choices and when the time of deploying a model has come.RECOMMENDED BOOKSPhil Winder • Reinforcement LearningKelleher & Tierney • Data Science (The MIT Press Essential Knowledge series)Lakshmanan, Robinson & Munn • Machine Learning Design PatternsLakshmanan, Görner & Gillard • Practical Machine Learning for Computer VisionAurélien 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: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted almost daily

In episode 36 of The Gradient Podcast, Daniel Bashir speaks to Sebastian Raschka.Sebastian is an Assistant Professor of Statistics at the University of Wisconsin-Madison and Lead AI Educator at Lightning AI. He has written two bestselling books: Python Machine Learning and Machine Learning with PyTorch and Scikit-Learn.Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterSections:(00:00) Intro(01:10) Sebastian's intro to AI(05:15) Sebastian's process for learning new things(12:15) Learning style varies with purpose(16:10) Ordinal Regression(31:00) Solving rank inconsistency with conditional probability(35:00) Semi-Adversarial Networks(44:15) Why Sebastian got into education(52:45) Lightning AI(1:00:00) Sebastian's advice for educators(1:03:30) Be cool like Sebastian and follow the Gradient(1:03:40) OutroEpisode Links:Sebastian's HomepageSebastian's TwitterSebastian's Books Get full access to The Gradient at thegradientpub.substack.com/subscribe

This interview was recorded at GOTO Copenhagen 2021 for GOTO Unscripted.gotopia.techRead the full transcription of this interview hereEkaterina Sirazitdinova - Data Scientist for Computer Vision, Video Analytics & Deep Learning at NVIDIAPrayson Daniel - Principal Data Scientist at NTT DATANicholai Stålung - Lead Data Scientist at TriforkDESCRIPTIONData science is so much more than collecting, sorting and analyzing data. What does it take to be a data scientist and how does a day in the life of a data scientist look like? Ekaterina Sirazitdinova, Prayson Daniel and Nicholai Stålung will give you an insight into this and more. RECOMMENDED BOOKSPhil Winder • Reinforcement LearningKelleher & Tierney • Data Science (The MIT Press Essential Knowledge series)Lakshmanan, Robinson & Munn • Machine Learning Design PatternsLakshmanan, Görner & Gillard • Practical Machine Learning for Computer VisionAurélien 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

In this episode, I am with Chip Kent, chief data scientist at Deephaven Data Labs. We speak about streaming data, real-time, and other powerful tools part of the Deephaven platform.   Links Deephaven - https://deephaven.io Deephaven Community Core Documentation - ​​https://deephaven.io/core/docs/ Deephaven Community Slack - https://join.slack.com/t/deephavencommunity/shared_invite/zt-11x3hiufp-DmOMWDAvXv_pNDUlVkagLQ GitHub: Deephaven Community Core - https://github.com/deephaven/deephaven-core Barrage - https://github.com/deephaven/barrage Deephaven web components - https://github.com/deephaven/web-client-ui YouTube Channel - https://www.youtube.com/channel/UCoaYOlkX555PSTTJz8ZaI_w Blog posts  Real-time classification with Deephaven and SciKit-Learn - https://deephaven.io/blog/2022/02/02/learn-scikit/ Display a quadrillion rows of data in the browser - https://deephaven.io/blog/2022/01/24/displaying-a-quadrillion-rows/ A performance comparison between Materialize and Deephaven - https://deephaven.io/blog/2022/03/05/deephaven-materialize-study/ Careers https://deephaven.io/company/careers/ Community Slack http://deephaven.io/slack. 

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

Sebastian Raschka is lead author of a new book from Packt entitled “Machine Learning with PyTorch and Scikit-Learn”.  He is also an Assistant Professor of Statistics at the University of Wisconsin (Madison), and serves as the Lead AI Educator at Grid.ai.  Download a FREE copy of our recent NLP Industry Survey Results:  https://gradientflow.com/2021nlpsurvey/Subscribe: Apple • Android • Spotify • Stitcher • Google • AntennaPod • RSS.Detailed show notes can be found on The Data Exchange web site.

Joining us today on How AI Happens is Sebastian Raschka, Lead AI educator at GRID.ai and Assistant Professor of Statistics at the University of Wisconsin-Madison.  Sebastian fills us in on the coursework he's creating in his role at GRID.ai, and we find out what can be attributed to the crossover of machine learning in academia and the private sector. We speculate on the pros and cons of the commodification of deep learning models and which machine learning framework is better: PyTorch or TensorFlow. Key Points From This Episode:Sebastian Raschka's journey from the computation of biology to AI and machine learning.The focus of his current role as Lead AI educator at GRID.ai.The ideal applications and outcomes of the coursework Sebastian is developing.The crossover of machine learning in academia and the private sector; the theory versus the application.Deep learning versus machine learning and what constitutes a deep learning problem.The importance of sufficient data for deep learning to be effective.The applications of the BERT text model.The pros and cons of developing more accessible models.Why Sebastian set out to write Machine Learning with PyTorch and Scikit-Learn.The structure of the book, including theory and application.Why Sebastian prefers PyTorch over TensorFlow.What he finds most exciting in the current deep learning space.The emerging opportunities to use deep learning!Tweetables:“In academia, the focus is more on understanding how deep learning works… On the other hand, in the industry, there are [many] use cases of machine learning.” — @rasbt [0:10:10]“Often it is hard to formulate answers as a human to complex questions.” — @rasbt [0:12:53]“In my experience, deep learning can be very powerful but you need a lot of data to make it work well.” — @rasbt [0:14:06]“In [Machine Learning with PyTorch and Scikit-Learn], I tried to provide a resource that is a hybrid between more theoretical books and more applied books.” — @rasbt [0:23:21]“Why I like PyTorch is that it gives me the readability [and] flexibility to customize things.” — @rasbt [0:25:55]Links Mentioned in Today's Episode:Sebastian RaschkaSebastian Raschka on TwitterGRID.aiMachine Learning with PyTorch and Scikit-Learn 

Today we're joined by Sebastian Raschka, an assistant professor at the University of Wisconsin-Madison and lead AI educator at Grid.ai. In our conversation with Sebastian, we explore his work around AI education, including the “hands-on” philosophy that he takes when building these courses, his recent book Machine Learning with PyTorch and Scikit-Learn, his advise to beginners in the field when they're trying to choose tools and frameworks, and more.  We also discuss his work on Pytorch Lightning, a platform that allows users to organize their code and integrate it into other technologies, before switching gears and discuss his recent research efforts around ordinal regression, including a ton of great references that we'll link on the show notes page below!  The complete show notes for this episode can be found at twimlai.com/go/565

This week we have scientific computing educator extraordinaire, Harrison Kinsley on the show! Harrison is the founder of multiple businesses, all of which leverage the Python programming language. From using Flask web development on all of his business sites to Scikit Learn and TensorFlow for machine learning and data analysis with Ensmo.com to the Natural Language Toolkit for natural language processing with Sentdex.com to teaching a massive variety of Python programming topics on PythonProgramming.net -- Python and programming is a major part of Harrison's life and work. Harrison likes to learn and build with technology which he teaches on his YouTube channel with over a million subscribers. Harrison believes programming is a superpower, and the social impact of making programming education easily accessible to anyone is one of the most important things he can do with his life. Harrison's Python Website: www.pythonprogramming.net Harrison's Python YouTube: www.youtube.com/c/sentdex Harrison's Twitter: www.twitter.com/sentdex Harrison's Instagram: www.instagram.com/sentdex Who is Puget Systems? Puget Systems is based in the Seattle suburb of Auburn, WA, and specializes in high-performance, custom-built computers. We believe that computers should be a pleasure to purchase and own. They should get your work done, and not be a hindrance. Our goal is to provide each client with the best possible computer for their needs and budget. Learn more about Puget Systems: www.pugetsystems.com Learn more about our Scientific Computing Solutions: www.pugetsystems.com/solutions/scientific/index.php --- Send in a voice message: https://anchor.fm/puget-systems/message

Have you been considering launching a product or even a business based on Python's AI / ML stack? We have a great guest on the episode this week, Dylan Fox, who is the cofounder of AssemblyAI and has been building his startup successfully over the past few years. He has interesting stories of 100s of GPUs in the cloud, evolving ML models, and much more that I know you'll enjoy hearing. Links from the show Dylan Twitter: @YouveGotFox AssemblyAI: assemblyai.com TensorFlow: tensorflow.org PyTorch: pytorch.org hugging face: huggingface.co SciKit-Learn: scikit-learn.org GeForce Card: nvidia.com pLS: twitter.com This journalist's Otter.ai scare is a reminder that cloud transcription isn't completely private: theverge.com Programming language trends: insights.stackoverflow.com Can My Water Cooled Raspberry Pi Cluster Beat My MacBook?: the-diy-life.com PyTorch vs TensorFlow in 2022: assemblyai.com/blog/pytorch-vs-tensorflow Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Stack Overflow Sentry Error Monitoring, Code TALKPYTHON Talk Python Training

https://go.dok.community/slack https://dok.community/ ABSTRACT OF THE TALK Complex computational workloads in Python are a common sight these days, especially in the context of processing large and complex datasets. Battle-hardened modules such as Numpy, Pandas, and Scikit-Learn can perform low-level tasks, while tools like Dask makes it easy to parallelize these workloads across distributed computational environments. Meanwhile, Argo Workflows offers a Kubernetes-native solution to provisioning cloud resources in Kubernetes and triggering workflows on a regular schedule. Being Kubernetes-native, Argo Workflows also meshes nicely with other Kubernetes tools. This talk discusses the combination of these two worlds by showcasing a set-up for Argo-managed workflows which schedule and automatically scale-out Dask-powered data pipelines in Python. BIO Former academic in the field of renewable energy simulation and energy systems analysis. Currently responsible for architecting and maintaining the cloud- and data strategy at ACCURE Battery Intelligence KEY TAKE-AWAYS FROM THE TALK Argo Workflows + Dask is a nice combination for data-processing pipelines. There are a a few "gotchyas" to be on the look-out for, but in nevertheless this is still a generally-applicable and powerful combination. https://github.com/sevberg

Думаю, для тех, кто следит за индустрией искусственного интеллекта, Валерий Бабушкин в особом представлении не нуждается. Специалист высочайшего класса, за которым охотятся самые крутые технологические компании! И, вот, сегодня Валерий в гостях Machine Learning Podcast. Поговорили о карьерном пути Валерия, о том, как бизнесу внедрять машинное обучение в свои процессы, какие книги читать, чтобы держать себя в тонусе, зачем фейсбук борется с альтернативными ватсап-клиентами, зачем нужен Kaggle в карьере дата-сайентиста и еще много о чем! Ссылки выпуска: Реферальная программа Intel, по которой можно получить 70000 рублей за каждого трудоустроенного, по вашей рекомендации, сотрудника в компанию (http://career.intel.com/tp/rj6_qk7Te_e_K) Вакансии компании Intel (http://career.intel.com/tp/rj6_itirV.e_K) Kaggle (https://www.kaggle.com/) Книга "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" (https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/) Книга "The Minimum Description Length Principle" (https://mitpress.mit.edu/books/minimum-description-length-principle) Книга "Нетрадиционные методы многомерного статистического анализа" (https://www.biznesbooks.com/books/ekonomika/efron-b-netradicionnye-metody-mnogomernogo-statisticheskogo-analiza) Буду благодарен за обратную связь! Поддерживайте подкаст на Patreon (https://www.patreon.com/machinelearningpodcast) Оставляйте ваши комментарии там, где можно. Например, в Apple Podcasts. Они помогут сделать подкаст лучше! Напишите что вам было понятно, что не очень, какие темы раскрыть, каких гостей пригласить, ну, и вообще в какую сторону катить этот подкаст :) Подписывайтесь на телеграм-канал "Стать специалистом по машинному обучению" (https://t.me/toBeAnMLspecialist) Телеграм автора подкаста (https://t.me/kmsint) Со мной также можно связаться по электронной почте: kms101@yandex.ru Также теперь подкаст можно найти на YouTube (https://www.youtube.com/channel/UCzvfXLNpB2Bbf32dc7a8oDQ?) и Яндекс.Музыке https://music.yandex.ru/album/9781458

Gemischte Dinge. Unter anderem Python 3.10. Jochen und Dominik haben sich mit dieser Episode etwas länger Zeit gelassen. Viel Kram zu tun. Das wird wieder besser. Versprochen. Shownotes Unsere E-Mail für Fragen, Anregungen & Kommentare: hallo@python-podcast.de News aus der Szene - Der Python 3.10 Release Stream - Official Python 3.10 Release - PEP 0617 zum neuen PEG-Parser - Helge Schneider über Werbung - Tribute to Sebastian Ramírez:     - FastAPI     - SQLModel     - Typer - Buch Elixir in Action - Inkrementelles black: darker - Guido zur Zukunft von Python. - Ein Struct in C. - High performance code execution engine: Python-piston. - RustPython - Django 4.0 - JavaScript Fetch in den MDN Web Docs und im Modern JavaScript Tutorial - SciKit Learn 1.0 - Fail2Ban CVE - Azure OMIGOD - Twitch Breach - Jochens Stream zur Implementierung eines Naive Bayes Spamfilters - htmx - high power tools for HTML - The Asset Pipeline in ruby on rails Picks - django-upgrade - textual / rich - humanize

こんにちは！今回は、scikit-learn 1.0がリリースされたので、その内容について話しました！ ======================== ◎関連リンク ・scikit-learn 1.0 リリースノート https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_0_0.html https://scikit-learn.org/dev/whats_new/v1.0.html ・バチェラー 始まる https://twitter.com/BachelorJapan/status/1443893459471962134 ・トド英語が待望のリリース https://apps.apple.com/JP/app/id1561705273?mt=8 ======================== ◎質問や感想、扱って欲しいトピックなど、お気軽にコメントお待ちしております. ・Googleフォーム http://urx.space/VNOD ・Twitterのハッシュタグ https://twitter.com/hashtag/wipfm ・Twitterアカウント https://twitter.com/wipfm0509 ======================== ◎"Work In Progress"とは 機械学習エンジニアの@takapyとデータアナリストの@yaginuuunが、テクノロジーやキャリア、ビジネスなどの話題についてカジュアルに話すPodcast番組です. 公式サイト: https://bit.ly/2UbGXIX @takapy: https://twitter.com/takapy0210 @yaginuuun: https://twitter.com/yaginuuun See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Watch the live stream: Watch on YouTube About the show Sponsored by us: Check out the courses over at Talk Python And Brian's book too! Special guest: Prayson Daniel Brain #1: Exciting New Ways To Be Told That Your Python Code is Bad Two new pylint errors consider-ternary-expression if condition(): x = 4 else: x = 5 x = 4 if condition() else 5 while-used it unconditionally flags every use of while expressions. generally, while should be avoided. Michael #2: GitHub Readme Stats via Роман Великий Dynamically generated stats for your github readmes This are for your repo or your stats (others too I suppose) posted somewhere outside of github Card for a project: https://github-readme-stats.vercel.app/api/pin/?username=mikeckennedy&repo=python-switch Card for a user: https://github-readme-stats.vercel.app/api?username=mikeckennedy&show_icons=true&theme=radical Card for your languages: https://github-readme-stats.vercel.app/api/top-langs/?username=mikeckennedy&repo=python-switch Prayson #3: Nox Nox appeared as “footnotes” in Episodes 182 and 248 (Hypermodern Python …) It does tox what invoke did (substituting GNU Make) Brian #4: Two tools for dealing with text python-easyfrontmatter - a small package to load and parse files (or just text) with YAML (or JSON, TOML or other) front matter. >>> post = frontmatter.load('tests/yaml/hello-world.txt') >>> print(post['title']) Hello, world! Tried it with a helper script I'm using with Hugo, and it parses Hugo metadata in blog posts like a dream. ftfy - fixes text for you “Take in bad Unicode and output good Unicode” >>> import ftfy >>> ftfy.fix_text('✔ No problems') '✔ No problems' Michael #5: MPIRE (MultiProcessing Is Really Easy) A Python package for easy multiprocessing, but faster than multiprocessing It combines the convenience of map like functions of multiprocessing.Pool with the benefits of using copy-on-write shared objects of multiprocessing.Process, together with easy-to-use worker state, worker insights, and progress bar functionality. Many features Requisite shoutout to unsync too. Prayson #6: skorch Going deep learning with scikit-learn pipelines (Breaking limits of multi-layer perceptron (MLP)) Using PyTorch, skorch provides an API to extend neural networks models in scikit-learn. Example: Penguins Classification shameless Gist Extras Michael vim + jupyter, via Marco Gorelli PyBay talk Prayson python-decouple Joke: Adoption

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

話した内容ScrapBox 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、データ分析コンペの賞金の税金、Scikit Learn 0.24、SIGNATE金融コンペ、Notion について話しています。

So, you've decided that you're ready to take the plunge and seriously upskill into data science & machine learning…but should you do a master's degree or a boot camp?   In this episode, I'm joined by Chris Sanchez, a retired Navy SEAL and fellow member of the Veterans in Data Science & Machine Learning community. The conversation starts with an overview of his journey through the military and into data science.   Chris' unique perspective extends beyond just his background with Special Operations Forces (SOF). He's also rather rare in the sense that he attended both a data science graduate program (UC Berkley's Master of Information and Data Science) and a data science boot camp (Galvanize Data Science Immersive Bootcamp).   Consequently, Chris and I dive deep into a candid compare & contrast of his experiences with both learning approaches, where each excelled, and where they didn't. Chris starts the comparison by telling us about the best qualities of each training approach. Then, we zoom in to continue the comparison, looking at seven key facets:   - Cost - Duration - Financing - Prerequisites - Pedagogy - Networking - Career Support Services   The episode wraps-up with Chris sharing some of his favorite data science & ML learning resources:   - DATAQUEST: https://www.dataquest.io/ - Learn Python 3 the Hard Way: https://www.amazon.com/Learn-Python-Hard-Way-Introduction-ebook/dp/B07378P8W6 - Andrew Ng's Stanford CS229 – Machine Learning: https://see.stanford.edu/Course/CS229 - Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646 - Fast.ai: https://www.fast.ai/ - Practical Deep Learning for Coders (Fast.ai's MOOC): https://course.fast.ai/   Go here to connect with Chris on LinkedIn (be sure to include a short contextual note with your connection request!): https://www.linkedin.com/in/excellenceisahabit/   Go here to see Chris' GitHub portfolio: https://americanthinker.github.io/   There is so much packed into this episode that it's impossible to put it all in the show notes.  One recommendation that I do have is that you listen to this podcast, then listen again while taking notes.    ===================================   Have a question for me?  Leave me a voicemail or send me a message at: http://www.vetsindatascience.com/thedatacanteen   The Data Canteen's parent organization: http://www.vetsindatascience.com/   Join the Veterans in Data Science & Machine Learning Community on LinkedIn: https://www.linkedin.com/groups/8989903/   SUPPORT THE DATA CANTEEN (LIKE PBS, WE'RE LISTENER SUPPORTED!): Donate: https://vetsindatascience.com/support-join

Your business is sitting on an untapped data goldmine. But how do you transform your organization into a mining machine? In the latest episode of Cloudspotting, three data experts join podcast hosts Alex and Sai to break down the complex topic of utilizing data in your organization. Show notes: Recommended read - The Unicorn Project by Gene Kim https://www.goodreads.com/en/book/show/44333183-the-unicorn-project Recommended read - Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow by Aurélien Géron https://www.goodreads.com/en/book/show/40363665-hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow Recommended read - The Rack We Built: The Good, The Bad, and the Ugly of Creating Company Culture by Lorenzo Gómez https://www.goodreads.com/book/show/55681761-the-rack-we-built Special Guests: Ben Morgan-Smith, Mark McQuade, and Nirmal Ranganathan.

In this episode of Adventures in Machine Learning, the panelists chat with Laurence Moroney about the history of AI in the UK. We talk about the AI overlords, ethics, and the need for teaching. Industry revolutions and how we can adapt them to improve life. Laurence is working on new synthetic datasets so tune in and check it out! Sponsors Machine Learning for Software Engineers by Educative.io Audible.com CacheFly Panel Charles Max Wood Gant Laborde Jason Mayes Daniel Svoboda Guest Laurence Moroney Picks Jason Mayes: TensorFlow.js Show and Tell on Youtube Gant Laborde: https://www.tensorflowtictactoe.co/ Daniel Svoboda: Youtube: Kaggle Laurence Moroney: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems The Manga Guide to Machine Learning Follow Adventures in Machine Learning on Twitter > @podcast_ml

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

In this episode, we talk about why the two libraries Scikit-Learn and Keras are great for machine learning. These two libraries combined with Pandas form the 3 core libraries in Python for a data scientist today. We cover things like: 1) Data Exploration and data cleaning - how Pandas and Jupyter notebooks provide a good way to get started here. 2) Data Transformation - how Scikit-Learn provides many useful functions like train_test_split, Scalers, PCA etc. 3) Data Fitting - how Scikit-Learn provides good shallow models and Keras provides great support to quickly get started with neural networks. We also cover various tidbits on things to take note in building ML pipelines and preparing models to be deployed in production, so tune into the episode to find out! Fantastic Resources: 1) Book by head of Youtube DS team Aurelien Geron: https://www.amazon.com/dp/1492032646/?tag=omnilence-20 This is one of the best book I have read on this topic as it covers practical tips incl. Scikit-Learn API etc. 2) Developing Scikit-Learn estimators: https://scikit-learn.org/stable/developers/develop.html 3) Guide to Keras Sequential API: https://keras.io/getting-started/sequential-model-guide/ 4) Guide to Keras Functional API: https://keras.io/getting-started/functional-api-guide/ 5) My previous episode on Pandas: https://podcasts.apple.com/us/podcast/17-why-pandas-is-the-new-excel/id1453716761?i=1000454831790 Thanks for listening! Please consider supporting this podcast from the link in the end. --- Send in a voice message: https://anchor.fm/the-data-life-podcast/message Support this podcast: https://anchor.fm/the-data-life-podcast/support

Now that we've covered how open source works, we're looking to pull back the curtain and see who's actually contributing. In part 2/2 of our series on open source, we sat down with Reshama Shaikh, a statistician and key organizer of scikit-Learn sprints, to learn about the ups & downs of open source contributing, as well how a Sprint in Nairobi benefits Fortune 500 companies in the US. Reshama Shaikh is an independent data scientist/statistician and MBA with skills in Python, R and SAS. I worked for over 10 years as a biostatistician in the pharmaceutical industry.Further Reading: Stack Overflow Developer Survey; Open Source Contributors: https://insights.stackoverflow.com/survey/2019#developer-profile-_-contributing-to-open-sourceHow to Organize a Scikit Learn Spring by Reshama Shaikh: https://reshamas.github.io/how-to-organize-a-scikit-learn-sprint/Reshama Shaikh's Website: https://reshamas.github.io/Contributing to scikit-Learn: https://scikit-learn.org/stable/developers/contributing.htmlGitter scikit-Learn: https://gitter.im/scikit-learn/scikit-learnscikitLearn Mailing List: https://mail.python.org/mailman/listinfo/scikit-learnJoin Dataiku's Paris scikit-Learn sprint this January: https://github.com/scikit-learn/scikit-learn/wiki/Paris-scikit-learn-Sprint-of-the-Decade

Open Source software such as scikit-Learn, Python, and Spark form the backbone of data science. In a two-part series, we're covering the ins and outs of open source - and how this special type of software supports 98% of enterprise-level companies' data science efforts.In part 1, we're chatting with Andreas Mueller, a core contributor of  scikit-Learn aboutthe  value in open source versus corporate software, and what it looks like to run and govern this type of community-written (and driven) project.Join our Paris scikit-Learn sprint this January: https://github.com/scikit-learn/scikit-learn/wiki/Paris-scikit-learn-Sprint-of-the-DecadeAndreas Mueller is a lecturer at the Data Science Institute at Columbia University and author of the O'Reilly book “Introduction to Machine Learning with Python”, describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and he has been co-maintaining it for several years. He is also a Software Carpentry instructor. In the past, he worked at the NYU Center for Data Science on open source and open science, and as Machine Learning Scientist at Amazon. You can find his full cv here. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.