intermediate

Introduction to JAX for Deep Learning

Artificial Intelligence

5 courses

Master JAX from the ground up! This path takes you from NumPy-like basics and automatic differentiation, through advanced batching and PyTrees, to building and training deep neural networks with Flax and Optax—culminating in a real-world image classification project.

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Verified skills you'll gain

INTERMEDIATE

Coding and Data Algorithms

ADVANCED

Deep Learning and Neural Networks

Tools you'll use

Flax

JAX

Optax

Python

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This course introduces JAX, a high-performance numerical computation library. You'll learn how JAX extends NumPy's familiar API with key features like immutability, pure functions, automatic differentiation for gradient-based optimization, and just-in-time (JIT) compilation for significant speedups on accelerators like GPUs and TPUs.

Course 2

Advanced JAX: Transformations for Speed & Scale

5 lessons

21 practices

Dive deeper into JAX's powerful functional transformations. This course covers explicit pseudo-random number generation for reproducibility, automatic vectorization with jax.vmap for batching, an introduction to jax.shard_map for multi-device parallelism, the concept of PyTrees for handling complex data structures, and basic profiling/debugging techniques.

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Course 3

JAX in Action: Neural Networks from Scratch

4 lessons

15 practices

This course guides you through building a simple Multi-Layer Perceptron (MLP) from scratch using only JAX and its NumPy API. You'll tackle the classic XOR problem, learning how to manage model parameters as PyTrees, implement the forward pass, define a loss function, compute gradients, and write a manual training loop with a basic optimizer step.

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Course 4

Beyond Pure JAX: Flax & Optax for Elegant ML

4 lessons

17 practices

Elevate your JAX deep learning projects with Flax and Optax. This course introduces Flax for concisely defining neural network architectures as Modules, and Optax for a comprehensive suite of optimizers. You'll refactor the XOR classifier from the previous course, leveraging these powerful libraries for more structured and maintainable code.

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Course 5

JAX in Action: Building an Image Classifier

4 lessons

17 practices

Apply your JAX, Flax, and Optax skills to a practical project: building an image classification pipeline. This course covers setting up a multi-file project, loading and preprocessing image data (e.g., MNIST), defining a Convolutional Neural Network (CNN) with Flax, and implementing robust training and evaluation loops.

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Senior Machine Learning Engineer

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I'm impressed by the quality and can't stop recommending it. It's also a lot of fun!

Francisco Aguilar Meléndez

Data Scientist

+11

From our community

Hear what our customers have to say about CodeSignal Learn

I'm impressed by the quality and can't stop recommending it. It's also a lot of fun!

Francisco Aguilar Meléndez

Data Scientist

+11

I love that it's personalized. When I'm stuck, I don't have to hope my Google searches come out successful. The AI mentor Cosmo knows exactly what I need.

Faith Yim

Software Engineer

+14

It's an amazing product and exceeded my expectations, helping me prepare for my job interviews. Hands-on learning requires you to actually know what you are doing.

Alex Bush

Full Stack Engineer

I'm really impressed by the AI tutor Cosmo's feedback about my code. It's honestly kind of insane to me that it's so targeted and specific.

Abbey Helterbran

Tech consultant

I tried Leetcode but it was too disorganized. CodeSignal covers all the topics I'm interested in and is way more structured.

Jonathan Miller

Senior Machine Learning Engineer

+12

I'm impressed by the quality and can't stop recommending it. It's also a lot of fun!

Francisco Aguilar Meléndez

Data Scientist

+11

Introduction to JAX for Deep Learning

Verified skills you'll gain

Trusted by learners working at top companies

Turn screen time into skills time

Join the 1M+ learners on CodeSignal

Certificate of Achievement

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