backpropagation 开源项目推荐（1,642 个）

HIPS /autograd

Efficiently computes derivatives of NumPy code.

codeplea /genann

simple neural network library in C99

chelsea0x3b /dfdx

Deep learning in Rust, with shape checked tensors and neural networks

Ewenwan /Mathematics

数学知识点滴积累 矩阵 数值优化 神经网络反向传播 图优化 概率论 随机过程 卡尔曼滤波 粒子滤波 数学函数拟合

lijqhs /deeplearning-notes

Notes for Deep Learning Specialization Courses led by Andrew Ng.

patrikeh /go-deep

Artificial Neural Network

milanm /AutoGrad-Engine

一个完整的GPT语言模型（训练和推理），用~600行纯C#，零依赖

ahmedbesbes /Neural-Network-from-scratch

Ever wondered how to code your Neural Network using NumPy, with no frameworks involved?

emilwallner /Deep-Learning-From-Scratch

Six snippets of code that made deep learning what it is today.

Jasonnor /Backpropagation

Implementing multilayer neural networks through backpropagation using Java.

sidroopdaska /SelfDrivingCar

A collection of all projects pertaining to different layers in the SDC software stack

snrazavi /Machine_Learning_2018

Tabriz 大学 2018 年秋季机器学习课程的代码与项目

mstksg /backprop

Heterogeneous automatic differentiation ("backpropagation") in Haskell

MorvanZhou /npnet

仅基于Numpy构建神经网络

mohammad-ghaderi /mnist-asm-nn

A complete neural network built entirely in x86 assembly language that learns to recognize handwritten digits from the MNIST dataset. No frameworks, no high-level languages - just pure assembly - ~5.3× faster than NumPy

shantanu1109 /Coursera-Imperial-College-of-London-Mathematics-For-Machine-Learning-Specialization

本库包含伦敦帝国理工学院在Coursera上提供的机器学习数学专业的测验和实验作业解答，该课程由David Dye、Samuel J. Cooper、A. Freddie Page、Marc Peter Deisenroth授课

Somnibyte /MLKit

A simple machine learning framework written in Swift 🤖

ADGEfficiency /teaching-monolith

Data science teaching materials

Ameobea /neural-network-from-scratch

A neural network library written from scratch in Rust along with a web-based application for building + training neural networks + visualizing their outputs

differential-machine-learning /notebooks

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.