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

My first ML sandbox

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

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

Neural Networks Fundamentals with Python – implementing neural networks from scratch

Implementation of feedforward-backpropogated Neural Network from Scratch

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

Neural nets for high accuracy multivariable nonlinear regression.

A step-by-step walkthrough of the inner workings of a simple neural network. The goal is to demystify the calculations behind neural networks by breaking them down into understandable components, including forward propagation, backpropagation, gradient calculations, and parameter updates.

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

Detailed python notes & code for lectures and exercises of Andrej Karpathy's course "Neural Networks: Zero to Hero." The course is focused on building neural networks from scratch.

XOR gate which predicts the output using Neural Network 🔥

Neural Network with VHDL and matlab

Learn machine learning the hard way