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a beautifully compact machine-learning library

Project description

froog unit test badge

froog the frog
froog: fast real-time optimization of gradients
a beautifully compact machine-learning library
homepage | documentation | pip

froog is extremely simple, with a goal of running ml on any device, by any human, easily and efficiently

Installation

pip install froog

Overview of Features

  • Tensors
  • Automatic Differentiation
    • Forward and backward passes
  • Input/gradient shape-tracking
  • MNIST example
  • 2D Convolutions (im2col)
  • Numerical gradient checking
  • The most common optimizers (SGD, Adam, RMSProp)

Math Operations

  • Scalar-Matrix Multiplication
  • Dot Product
  • Sum
  • ReLU
  • Log Softmax
  • 2D Convolutions
  • Avg & Max pooling
  • More

Bounties

Want to help but don't know where to start? Here are some bounties for you to claim

Small

  • binary cross entropy
  • flatten
  • batch_norm
  • div
  • pow
  • dropout

Medium

  • start doing ops with opencl
  • simplify how context and gradients are handled
  • efficient net

Large

  • transformers
  • stable diffusion
  • winograd convs
  • MPS support
  • CUDA support

Contributing

Here are the rules for contributing:

  • increase simplicity
  • increase efficiency
  • increase functionality

more info on contributing

Project details


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Source Distribution

froog-0.1.8.tar.gz (16.2 kB view hashes)

Uploaded Source

Built Distribution

froog-0.1.8-py3-none-any.whl (20.1 kB view hashes)

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