HomeCoursesIntroduction to JAX and Deep Learning

4.6

Intermediate

2h 30min

Introduction to JAX and Deep Learning

Discover the power of JAX in deep learning. Gain insights into its ecosystem and learn about linear algebra, pseudo-random number generation, and optimization algorithms for cleaner, structured coding.

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Overview

Content

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JAX is a Python library designed for high-performance ML research. It is a powerful numerical computing library, just like Numpy, but with some key improvements. In this course, you will learn all about JAX and its ecosystem of libraries (Haiku, Jraph, Chex, Flax, Optax). Addressing a wide range of audiences, you will cover several topics including linear algebra, random variables theory, pseudo-random number generation, and optimization algorithms. By the end of this course, you will have a new set of skills that will make deep learning programming more intuitive, structured, and clean.

JAX is a Python library designed for high-performance ML research. It is a powerful numerical computing library, just like Numpy...Show More

WHAT YOU'LL LEARN

Learn the basics of JAX

Learn how to apply Autograd

Use auto vectorization for batching

Use Haiku and Flax for implementing neural networks

Cover Optax and overview of common optimization algorithms in deep learning

Use Chex for testing JAX programs

Learn the basics of applied linear algebra

Learn random variables theory and probability distributions

Learn pseudo-random number generation

Cover the basics of optimal transport

Learn the basics of JAX

TAKEAWAY SKILLS

Random Variables

Neural Networks

Functional Programming

Deep learning basics

Content

53 Lessons1 Project7 Quizzes14 Challenges

Introduction

2 Lessons

Get familiar with JAX, a powerful library for deep learning and numerical computing.

Course Introduction

JAX Overview

JAX Programming Model

9 Lessons

Walk through JAX's programming model, including pure functions, JIT, jaxpr, and autodiff.

Pure Functions

JAX and NumPy

Just-in-Time (JIT) Compilation

JAX Expressions (Jaxpr)

Asynchronous Dispatch

Auto-differentiation

Pytrees

Quiz: Basics of JAX

Challenge: Basics of JAX

Linear Algebra

15 Lessons

Explore the fundamental concepts of vectors, matrices, multivariate calculus, and convolutions in deep learning.

Random Variables and Distributions

7 Lessons

Grasp the fundamentals of random variables, distributions, PRNGs, and divergence measures in JAX.

JAX Ecosystem

14 Lessons

Take a closer look at the tools and libraries within the JAX ecosystem for deep learning.

Project: GAN Using the JAX ecosystem

Project

Appendix

6 Lessons

Focus on installation steps, notable JAX libraries, models, vector calculus, common errors, and key terms.

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

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