February 3, 2022

Training your neural network ten times faster using Jax on a TPU

Jasper Van den Bossche
Software Engineer
No items found.
Subscribe to newsletter
Share this post

This blog post explores the benefits of using the JAX library for machine learning and scientific computing. It starts by discussing the limitations of traditional Python libraries like NumPy and explains how JAX overcomes these limitations. It then goes on to highlight some of the unique features of JAX, such as automatic differentiation and just-in-time (JIT) compilation.

You xpect to learn about the advantages of using JAX for machine learning and scientific computing, including faster performance and improved memory efficiency. You will also gain an understanding of how JAX's automatic differentiation and JIT compilation capabilities can help streamline their workflow and make it easier to experiment with different models and algorithms.

The post includes code examples and explanations of how to use JAX for various tasks, such as training a neural network and calculating gradients. By the end of the post, you will  have a good understanding of what JAX is, how it works, and why it is a valuable tool for machine learning and scientific computing.

You can find the blogpost on our Medium channel here.

Related posts

View all
No results found.
There are no results with this criteria. Try changing your search.
Large Language Model
Foundation Models
Structured Data
Chat GPT
Voice & Sound
Front-End Development
Data Protection & Security
Responsible/ Ethical AI
Hardware & sensors
Generative AI
Natural language processing
Computer vision