The journey into the world of machine learning can often feel daunting. But don’t worry! With JAX, Flax, and Haiku at your disposal, you have a powerful toolkit to get started. This article will guide you through the essentials of using JAX and its ecosystem effectively.

Table of Contents

• Machine Learning with JAX
• Tutorial #1: From Zero to Hero
• Tutorial #2: From Hero to Hero Pro+
• Tutorial #3: Coding a Neural Network from Scratch in Pure JAX
• Tutorial #4: Flax From Zero to Hero
• Tutorial #5: Haiku From Zero to Hero (coming soon)
• Other useful JAX resources

My Machine Learning with JAX Tutorials

To make your learning journey seamless, I highly recommend utilizing Google Colab for running JAX notebooks directly. This means you can skip the hassles of setting up a Python environment, particularly useful if you’re on an unsupported OS like Windows!

Tutorial #1: From Zero to Hero

Dive into the world of JAX by starting with the fundamentals. This tutorial will cover basics and gradually uncover intricate details such as jit, grad, vmap, and more peculiarities of JAX.

• Watch Video (Tutorial #1)
• Access Accompanying Jupyter Notebook

Tutorial #2: From Hero to Hero Pro+

Level up your ML skills by learning how to train models across multiple machines, even on 8 TPU cores!

• Watch Video (Tutorial #2)
• Access Accompanying Jupyter Notebook

Tutorial #3: Building a Neural Network from Scratch

In this engaging tutorial, watch the process of coding a neural network from the ground up. You’ll explore:

• The implementation of a simple MLP (Multilayer Perceptron)
• Model training as a classifier on the MNIST dataset
• Visualization of learned weights and embeddings using t-SNE
• Analysis of dead ReLU neurons in your network

• Watch Video (Tutorial #3)
• Access Accompanying Jupyter Notebook

Tutorial #4: Machine Learning with Flax – From Zero to Hero

This tutorial introduces you to Flax, covering essential components like init, apply, and TrainState.

• Watch Video (Tutorial #4)
• Access Accompanying Jupyter Notebook

Tutorial #5: Coming Up – Machine Learning with Haiku

Stay tuned for the fifth tutorial that will soon cover Haiku!

Other Useful Content

For further exploration, don’t miss out on these valuable resources:

Videos

• Introduction to JAX
• JAX: Accelerated Machine Learning Research – SciPy 2020
• NeurIPS 2020: JAX Ecosystem Meetup
• Introduction to JAX for Machine Learning and More

Blogs

• Using JAX to accelerate our research – DeepMind
• You don’t know JAX – Colin Raffel

Troubleshooting Tips

If you encounter issues while using JAX or any of the tutorials, consider the following:

• Ensure your JAX environment is properly set up. Follow the [official JAX documentation](https://jax.readthedocs.io) if unsure.
• Check if you have the latest version of JAX and its dependencies installed.
• Consult the JAX community for support on forums or platforms like Discord.

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