JAX – Andrew Fairless, Ph.D.

What I Read: Learning JAX

By Andrew Fairless on January 31, 2024November 20, 2023

https://kidger.site/thoughts/torch2jax/ Learning JAX as a PyTorch developerPatrick KidgerPosted on November 9, 2023 “Assuming you already know PyTorch, this is what I’ve found that you need to know to get upContinue readingWhat I Read: Learning JAX

What I Read: introduction to NumPyro

By Andrew Fairless on September 19, 2022August 22, 2022

https://dfm.io/posts/intro-to-numpyro/ An astronomer’s introduction to NumPyroDan Foreman-MackeyJul 28 2022 “In this post… I’ll focus primarily on providing an introduction to NumPyro, which is a probabilistic programming library that provides anContinue readingWhat I Read: introduction to NumPyro

What I Read: Engineering Trade-Offs in Automatic Differentiation

By Andrew Fairless on March 8, 2022January 20, 2022

https://www.stochasticlifestyle.com/engineering-trade-offs-in-automatic-differentiation-from-tensorflow-and-pytorch-to-jax-and-julia/ Engineering Trade-Offs in Automatic Differentiation: from TensorFlow and PyTorch to Jax and JuliaDecember 25 2021Author: Christopher Rackauckas “To understand the differences between automatic differentiation libraries, let’s talk about theContinue readingWhat I Read: Engineering Trade-Offs in Automatic Differentiation

What I Read: Representation quality, complexity

By Andrew Fairless on July 29, 2021July 12, 2021

https://willwhitney.com/representation-quality-and-the-complexity-of-learning.html Will WhitneyRepresentation quality and the complexity of learning “In the last few years, there’s been an explosion of work on learning good representations of data…. However, defining precisely whatContinue readingWhat I Read: Representation quality, complexity

What I Read: Parallelizing neural networks, GPU, JAX

By Andrew Fairless on July 28, 2021July 12, 2021

https://willwhitney.com/parallel-training-jax.html Will WhitneyParallelizing neural networks on one GPU with JAX How you can get a 100x speedup for training small neural networks by making the most of your accelerator.“Most neuralContinue readingWhat I Read: Parallelizing neural networks, GPU, JAX

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