64 Open Source Backpropagation Software Projects
Free and open source backpropagation code projects including engines, APIs, generators, and tools.
A collection of all projects pertaining to different layers in the SDC software stack
DSL for forward and reverse mode automatic differentiation in Haskell. Port of DiffSharp.
Recurrent JS16 ⭐
[INACTIVE] Amazingly simple to build and train various neural networks. The library is an object-oriented neural network approach (baked with Typescript), containing stateless and stateful neural network architectures.
Machine Learning In Python Workshop83 ⭐
My workshop on machine learning using python language to implement different algorithms
Minimalistic Multiple Layer Neural Network From Scratch In Python24 ⭐
Minimalistic Multiple Layer Neural Network from Scratch in Python.
V Iashin Cs231n44 ⭐
PyTorch/Tensorflow solutions for Stanford's CS231n: "CNNs for Visual Recognition"
Fotisk07 Deep Learning Coursera120 ⭐
Projects from the Deep Learning Specialization from deeplearning.ai provided by Coursera
Codes and Project for Machine Learning Course, Fall 2018, University of Tabriz
Learning Lab C Library18 ⭐
This library provides a set of basic functions for different type of deep learning (and other) algorithms in C.This deep learning library will be constatly updated
Rubixml Sentiment59 ⭐
An example project using a feed-forward neural network for text sentiment classification trained with 25,000 movie reviews from the IMDB website.
Ahmedbesbes Neural Network From Scratch245 ⭐
Ever wondered how to code your Neural Network using NumPy, with no frameworks involved?
Backpropagation Cnns10 ⭐
Deep learning for pedestrians: backpropagation in CNNs. Latex and PyTorch code to verify theoretical derivations.
Computer code collated for use with Artificial Intelligence Engines book by JV Stone
适合新手学习的神经网络实践教程+代码。a awesome neural network practice project for newbie.我的CSDN博客：
Theoretical Proof Of Neural Network Model And Implementation Based On Numpy68 ⭐
This resource implements a deep neural network through Numpy, and is equipped with easy-to-understand theoretical derivation, mainly for the in-depth understanding of neural networks. 神经网络模型的理论证明与基于Numpy的实现。
Mlp Training For Mnist Classification14 ⭐
Multilayer perceptron deep neural network with feedforward and back-propagation for MNIST image classification using NumPy
Whitebox Part134 ⭐
In this part, I've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)
Differential Machine Learning Notebooks69 ⭐
Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.
All Optical Neural Networks16 ⭐
Supporting code for "End-to-end optical backpropagation for training neural networks".
The Math Behind A Neural Network120 ⭐
Mathematics paper recapitulating the calculus behind a neural network and its back propagation
Rnn Rc Chaos36 ⭐
RNN architectures trained with Backpropagation and Reservoir Computing (RC) methods for forecasting high-dimensional chaotic dynamical systems.
Complement the article 'Differential Machine Learning' (Huge & Savine, 2020), including mathematical proofs and important implementation details for production
A framework for mini neural networks (perceptrons), written from scratch in python. The goal of the project is to demystify the workings of a neural network and various training algorithms by providing code written from scratch for the simplest neural network one could have.
A new lightweight auto-differentation library that directly builds on numpy. Used as a homework for CMU 11785/11685/11485.
Ameobea Neural Network From Scratch16 ⭐
A neural network library written from scratch in Rust along with a web-based application for building + training neural networks + visualizing their outputs
Code base for solving Markov Decision Processes and Reinforcement Learning problems using Recurrent Convolutional Neural Networks.
A simple multi-layer feed-forward neural network with backpropagation built in Swift.
Computational Graph10 ⭐
Efficiently performs automatic differentiation on arbitrary functions. Basically a rudimentary version of Tensorflow.
The only dynamic and reconfigurable Artificial Neural networks library with back-propagation for arduino
Py Cgraph10 ⭐
:tangerine: Intro to symbolic computation in Python including applications to function optimization, physics simulation and more. Includes notebooks on back-propagation, auto-diff and more.