I wrote an autodiff in C++ and implemented LeNet with it
I wrote an autodiff in C++ and implemented LeNet with it launched on October 18, 2024 with 36 votes, #723 of 5,185 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
This project is an automatic differentiation library written in C++ with a LeNet neural network implementation demonstrating its capabilities. It is designed for developers interested in understanding how autodiff systems work and how to build neural networks from scratch. The implementation shows practical application of gradient computation, making it useful for educational purposes or as a foundation for machine learning experimentation in C++.
written from the facts on this page · September 2026
From the sources
GitLab.com
Does a similar job
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Boldly go where Gradient Descent has never gone before with DiscoGrad2024 · github.com · ▲232Trying to do gradient descent using automatic differentiation over branchy programs? Or to combine them with neural networks for end-to-end training? Then this might be interesting to you. We develped DiscoGrad, a tool for automatic differentiation through C++ programs involving input-dependent control flow (e.g., "if (f(x) < c) { ... }", differentiating wrt. x) and randomness. Our initial motivation was to enable the use of gradient descent with simulations, which often rely heavily on such discrete branching. The latter makes plain autodiff mostly useless, since it can only account for the…
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