There are 7 repositories under neural-networks-from-scratch topic.
Data science teaching materials
A neural network library written from scratch in Rust along with a web-based application for building + training neural networks + visualizing their outputs
🤖 A TypeScript version of karpathy/micrograd — a tiny scalar-valued autograd engine and a neural net on top of it
An Open Convolutional Neural Network Framework in C++ From Scratch
Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity
Lightweight, easy to use, micro neural network framework written in Rust w/ no python dependencies
Let's build Neural Networks from scratch.
Matrix-Vector Library Designed for Neural Network Construction. cuda (gpu) support, openmp (multithreaded cpu) support, partial support of BLAS, expression template based implementation PTX code generation identical to hand written kernels, and support for auto-differentiation
This is my first Deep Learning project, which is a MNIST hand-written digits classifier. The model is implemented completely from scratch WITHOUT using any prebuilt optimization like Tensorflow or Pytorch. Tensorflow is imported only to load the MNIST data set. This model also uses 2 hidden layers with Adaptive Moment Optimization (Adam) and Drop-out regularization.
Learn to build neural networks from scratch, simply. No autograd, no deep learning libraries - just numpy.
XOR gate which predicts the output using Neural Network :fire:
My first ML sandbox
Pure Python Simple Neural Network (SNN) library
Neural Network with VHDL and matlab
Neural nets for high accuracy multivariable nonlinear regression and classification.
To understand neural networks thoroughly I implemented them from scratch in C++. This is the source code for the same.
PyTorch implementation of Neural Style Transfer
Implementing Neural Networks using Maths and Numpy only
Neural Networks and optimizers from scratch in NumPy, featuring newer optimizers such as DemonAdam or QHAdam.
Learn machine learning the hard way
Implementation of artificial neural networks
Multilayer Perceptron from scratch in python
An implementation of the NEAT (Neuroevolution through augmenting topologies) algorithm in Java. Originally found at http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf
This is a simple neural network using c++ language
A set of Jupyter notebooks implementing simple neural networks described in Michael Nielsen's book.
Implementation of George Hotz's tinygrad.
Code for my youtube video: Neural Network Crash Course, Ep 1
NTHU EE6550: Machine Learning
A C++ library using OpenGL 3 to build and run neural network on any GPU.