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Python vs Rust for Neural Networks | Nathan Goldbaum

ngoldbaum.github.io · 3,006 words · saved by 1 readers

In a previous post I introduced the MNIST dataset and the problem of classifying handwritten digits. In this post I’ll be using the code I wrote in that post to port a simple neural network implementation to rust. My goal is to explore performance and ergonomics for data science workflows in rust. Chapter 1 of the book describes a very simple single-layer Neural Network that can classify handwritten digits from the MNIST dataset using a learning algorithm based on stochastic gradient descent. This sounds complicated — and it kind of is, this stuff was state-of-the-art in the mid 1980s — but really it all comes down to about 150 lines of heavily commented Python code. I’m going to assume that you already know the content of that chapter so stop here and go read that if you want to brush up on neural network basics. Or don’t and just pay attention to the code, it’s not super important to understand the details of exactly why the code works the way it does to see the differences between t

Python vs Rust for Neural Networks | Nathan Goldbaum Nathan Goldbaum About Blog Contact Résumé Python vs Rust for Neural Networks In a previous post I introduced the MNIST dataset and the problem of classifying handwritten digits. In this post I'll be using the code I wrote in that post to port a simple neural network implementation to rust. My goal is to explore performance and ergonomics for data science workflows in rust. The Python Implementation Chapter 1 of the book describes a very simple single-layer Neural Network that can classify handwritten digits from the MNIST dataset using a lea

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