ML-From-Scratch Offers Accessible NumPy Implementations of ML Algorithms
The ML-From-Scratch repository provides bare-bones NumPy implementations of fundamental machine-learning models and algorithms, spanning linear regression through deep learning. Its stated focus is transparency and accessibility rather than optimized or computationally efficient implementations, with examples including CNN classification, GANs, deep Q-networks and clustering.
The project gives learners and developers a way to inspect the internal workings of common machine-learning algorithms in readable Python and NumPy code.
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.
Machine Learning From Scratch
About
Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.
The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible but rather to present the inner workings of them in a transparent and accessible way.