Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Implements common data science methods and machine learning algorithms from scratch in python. Intuition and theory behind the algorithms is also discussed.

🤖 Python implementations of some of the fundamental Machine Learning models and algorithms from scratch with interactive Jupyter demos and math being explained.

This repository is dedicated to building ML & DL algorithms from scratch

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

Machine Learning algorithms implementation in Python from scratch.

A tiny deep neural network framework developed from scratch in C++ and CUDA.

Machine learning & deep learning implementation from scratch, depending only on numpy.

This project implements the machine learning algorithms from scratch and compares the implementation with sklearn.

Machine learning algorithms from scratch

Some Machine Learning algorithms implemented by me, mostly from scratch

Jupyter Notebooks containing implementations of different ML models from scratch and with sklearn

Keras-style machine learning framework for Java

Bare-bone and simple implementations of few Machine Learning Algorithms

Simple and minimal Python implementation of Machine Learning algorithms and models.

A machine learning library created from scratch with Rust. It focuses on deep learning and neural networks, providing efficient implementations of popular ML algorithms.

Development of a Neural Network from scratch to predict divorce in marriages.

Implementation of popular machine learning algorithms built entirely from scratch using Python and Numpy only.

This project deals with implementation of various machine learning models from scratch in python( jupyter notebook) without actually importing them from the sklearn library.