machine-learning-from-scratch

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.

Become skilled in Artificial Intelligence, Machine Learning, Generative AI, Deep Learning, Data Science, Natural Language Processing, Reinforcement Learning and more with this complete 0 to 100 repository.

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

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

Machine Learning algorithms implementation in Python from scratch.

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.

Keras-style machine learning framework for Java

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

Some Machine Learning algorithms implemented by me, mostly from scratch

Bare-bone and simple implementations of few Machine Learning Algorithms

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

A robust K-Nearest Neighbors (KNN) classifier implemented entirely from scratch using only NumPy, evaluated on the Digits dataset.

Everything related to Data Science is covered from scratch as well as by using libraries.

A simple machine learning library.

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