📚 Learn about distributed training from Google Cloud Developer Advocate Nikita Namjoshi! 🌟 Data Parallelism vs Model Parallelism 🌟 Synchronous vs Asynchronous Data Parallelism …and more. Watch now → goo.gle/3JxYSii

💡 Different Probability Distributions: hubs.la/Q0118KW30 #Probability #DataScience #Statistics

If you'd like to learn the The Julia Language and you know Python, then this tutorial Colab notebook is for you. I just updated it to the latest Julia 1.7.1 version: colab.research.google.com/github/ageron/… Enjoy! 🙂

🎊🥳TensorFlow Similarity 0.15 is out and it's a big one ! It adds all you need to train self-supervised models with STOA algorithms. To get started head out to the hello world notebook → github.com/tensorflow/sim… #TensorFlow #DeepLearning TensorFlow François Chollet Jeff Dean

📈 Want an easier way to work with graph structured data that encourages collaboration? Meet TensorFlow Graph Neural Networks! TF-GNN provides extensive tooling around working with graph data, modeling APIs, and example models for quick onboarding. → goo.gle/3ngCGQ5

Understanding how neural networks reach decisions can be challenging, but is also valuable for uses from image analysis to scientific discovery. A new approach, called StylEx, discovers and visualizes how disentangled attributes affect a classifier → goo.gle/357gn9r

«Introduction to Probability for Data Science» by Stanley H. Chan, with video lectures, exercises, and Python, MATLAB, Julia, R code! 🤩🤩🤩 probability4datascience.com

Geometric & Graph ML were a 2021 highlight, with exciting fundamental research & high-profile applications. Michael Bronstein and I interviewed distinguished experts to review this progress & predict 2022 trends. It's our longest post yet! See 🧵 for summary. michael-bronstein.medium.com/predictions-an…

This is a big deal… awesome work from the team!!

The Early Release of the 3rd edition of my book is now available! homl.info/er3 🎉 It currently includes the first 9 chapters, raw & unedited. It's the same overall structure as the 2nd edition, but with many improvements:

I just got a hardcopy of my own book - it finally feels real :) I'm very happy with the print quality (see attached photo) - but you can still get the pdf for free from probml.ai if you prefer.

Letitia's AI YouTube channel is one of my favorite: excellent in-depth explanations at the right pace, many interesting insights, and a good dose of humour. You should subscribe now!

"Practical JAX" video, showing how JAX works code in real projects: youtu.be/ABQOOaxLFzI Hopefully, this is a useful step beyond the basic introduction / 'key principles' videos (which are definitely things that people should watch, but may seem rather abstract)

I am delighted to announce that the camera-ready version of my new book, "Machine Learning: Advanced Topics", is finally available online for free at probml.github.io/book2 (The MIT Press @mitpress.bsky.social will publish the hard copy in 2023.)

Today, Generative AI takes its next big step forward. Introducing Gen-1: a new AI model that uses language and images to generate new videos out of existing ones. Sign up for early research access: bit.ly/3RxvBZr

The future begins #gen1

Moving impressionism #gen1

Stories never change. Only the ways we tell them. Last night we celebrated a new era of storytelling at our first annual AI Film Festival. Congratulations again to all our winners. Visit aiff.runwayml.com to learn more.

Hey folks! 👋 At Hugging Face, we're organizing a community sprint to train dreambooth models with Keras. It starts on 7th of March 🌌 Details 👉github.com/huggingface/co…

#fullcircle