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

Deep learning library in python from scratch

Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity

Neural Networks Fundamentals with Python – implementing neural networks from scratch

deep learning from scratch. uses numpy/cupy, trains in GPU, follows pytorch API

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

XOR gate which predicts the output using Neural Network 🔥

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 Dro…

Learn to build neural networks from scratch, simply. No autograd, no deep learning libraries - just numpy.

Pure Python Simple Neural Network (SNN) library

Neural Network with VHDL and matlab

My first ML sandbox

Neural nets for high accuracy multivariable nonlinear regression and classification.

JABACAT-created machine learning library from scratch.

To understand neural networks thoroughly I implemented them from scratch in C++. This is the source code for the same.