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Engineering Trade-Offs in Automatic Differentiation: from TensorFlow and PyTorch to Jax and Julia - Stochastic Lifestyle
December 25 2021 in Julia, Programming, Science, Scientific ML | Tags: automatic differentiation, compilers, differentiable programming, jax, julia, machine learning, pytorch, tensorflow, XLA | Author: Christopher Rackauckas To understand the differences between automatic differentiation libraries, let’s talk about the engineering trade-offs that were made. I would personally say that none of thes
Victor MARTIN ~ Understanding Automatic Differentiation in 30 lines of Python
---> For French version of this article, click here I'm a Machine Learning engineer and I use libraries like Tensorflow and Pytorch in my work to train my neural networks. And it's been a while since I wanted to write the simplest piece of code to perform what is called automatic differentiation which is at the heart of neural network training. In this article, I will try to iteratively build the
GitHub - obround/mytorch: Automatic differentiation implemented in python, inspired by Pytorch (easily extensible)
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GitHub - pymanopt/pymanopt: Python toolbox for optimization on Riemannian manifolds with support for automatic differentiation
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