A deep learning framework created from scratch with Python and NumPy

Scripts of Machine Learning Algorithms from Scratch. Implementations of machine learning models and algorithms using nothing but NumPy with a focus on accessibility. Aims to cover everything from basic to advance.

The sample code to study non-negative matrix and tensor factorization.

React implementation in Python 3, which runs on the client-side.

Reinforcement Learning (RL)-based routing algorithm for SDN networks created from scratch using Python.

Convolutional Neural Network implemenation from scratch in python numpy

Implements Decision tree classification and regression algorithm from scratch in Python.

ML Algorithm implementation from scratch for practice

NeuralNetworkFromScratch

VyomAI: state-of-the-art NLP LLM Vision MultiModel transformers implementation into Pytorch

A k-nearest neighbors algorithm is implemented in Python from scratch to perform a classification or regression analysis.

This implementation is based on the paper titled "Conditional Text Image Generation with Diffusion Models," which can be found at arXiv:2306.10804v1.

Implementation of a simple neural language model (multi-layer perceptron) from scratch for next word prediction

Compiler for Scrape

Pytorch Lightning Implementation of StyleGAN

an artificial neural network framework built from scratch using just Python and Numpy

Neural Network build with numpy and math (no pytorch, keras etc.) for the MNIST dataset

Generalised neural network implemented from scratch in python to teach step-wise functioning of a neural network and back-propagation training algorithm for optical character recognition (OCR)

A Python script to interact with the Scratch API

Variational Autoencoder and a Disentangled version (beta-VAE) implementation in PyTorch-Lightning