![]() Gain access to Jupyter Notebooks for this tutorial and other PyImageSearch guides that are pre-configured to run on Google Colab’s ecosystem right in your web browser! No installation required.Īnd best of all, these Jupyter Notebooks will run on Windows, macOS, and Linux! Then join PyImageSearch University today! Ready to run the code right now on your Windows, macOS, or Linux system?.Wanting to skip the hassle of fighting with the command line, package managers, and virtual environments?.Learning on your employer’s administratively locked system?.Having Problems Configuring Your Development Environment?įigure 1: Having trouble configuring your dev environment? Want access to pre-configured Jupyter Notebooks running on Google Colab? Be sure to join PyImageSearch University - you’ll be up and running with this tutorial in a matter of minutes. If you need help configuring your development environment for OpenCV, we highly recommend that you read our pip install OpenCV guide - it will have you up and running in a matter of minutes. Luckily, jaxlib and jax are pip-installable: $ pip install jaxlib JAX is written in pure Python, but it depends on XLA, which needs to be installed as the jaxlib package (from: jax repository). To follow this guide, you need to have the JAX library installed on your system. Let’s get started and learn all about it! Major companies like Google Research, Hugging Face, and OpenAI are already using JAX heavily, so this is a valuable skill to have. Once you complete this course, you’ll be able to understand and work with any code written in JAX/FLAX. We’ll keep the language simple and avoid using jargon, but if you need help understanding anything, please let us know, and we’ll do our best to help. In this series, we’ll not only teach you about JAX, but also how to learn and understand new concepts. Many people have asked us to create a course about JAX, so we decided to take on the challenge. Recently, many people have been talking about JAX, a new numerical computing library that can make your code run faster. New academic papers and models are always coming out there’s a new framework to learn every few years. Learning JAX in 2023: Part 1 - The Ultimate Guide to Accelerating Numerical Computation and Machine LearningĪs deep learning practitioners, it can be tough to keep up with all the new developments. Looking for the source code to this post? Jump Right To The Downloads Section
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