Neural Network from Scratch

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com

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  • micrograd

    A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

  • For autograd from scratch, see https://github.com/karpathy/micrograd and/or

    https://windowsontheory.org/2020/11/03/yet-another-backpropa...

  • ML-From-Scratch

    Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

  • Interesting find. Just FYI, this repo has been the OG for several years, when it comes to building NN from scratch:

    https://github.com/eriklindernoren/ML-From-Scratch

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  • deepnet

    Educational deep learning library in plain Numpy. (by parasdahal)

  • For those interested in simple neural networks to CNN and RNNs implemented with just Numpy (including backprop):

    https://github.com/parasdahal/deepnet

  • machine.academy

    Neural Network training library in C++ and C# with GPU acceleration

  • Nice! I made a gpu accelerated backpropagation lib a while ago to learn about NNs, if you are interested check it out here: https://github.com/zbendefy/machine.academy

  • NNfSiX

    Neural Networks from Scratch in various programming languages

  • deeplearning-notes

    Notes for Deep Learning Specialization Courses led by Andrew Ng.

  • I found Andrew Ng’s Deep Learning Specialization much better for understanding neural networks than the machine learning course. https://www.coursera.org/specializations/deep-learning

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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