Beyond Backpropagation - Higher Order, Forward and Reverse-mode Automatic Differentiation for Tensorken

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Deep Learning neural-network Autograd GPU Numpy

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  • tensorken

    A fun, hackable, GPU-accelerated, neural network library in Rust, written by an idiot

    • This post describes how I added automatic differentiation to Tensorken. Tensorken is my attempt to build a fully featured yet easy-to-understand and hackable implementation of a deep learning library in Rust. It takes inspiration from the likes of PyTorch, Tinygrad, and JAX.

  • tinygrad

    You like pytorch? You like micrograd? You love tinygrad! ❤️

    • This post describes how I added automatic differentiation to Tensorken. Tensorken is my attempt to build a fully featured yet easy-to-understand and hackable implementation of a deep learning library in Rust. It takes inspiration from the likes of PyTorch, Tinygrad, and JAX.

  • Scout Monitoring

    Free Django app performance insights with Scout Monitoring. Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.

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  • swift

    The Swift Programming Language

    • Swift's Differentiable Programming Manifesto. Swift has a powerful differentiable programming component, integrated with the compiler.

  • Pytorch

    Tensors and Dynamic neural networks in Python with strong GPU acceleration

    • This post describes how I added automatic differentiation to Tensorken. Tensorken is my attempt to build a fully featured yet easy-to-understand and hackable implementation of a deep learning library in Rust. It takes inspiration from the likes of PyTorch, Tinygrad, and JAX.

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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