🧠 Hands-on AI & ML guide: from tensors to neural networks, with code, formulas, and model evaluation.

Large Scale Machine/Deep Learning library for Python

How to build a simple neural network from scratch using Numpy and linear algebra without relying on high-level libraries like TensorFlow or Keras.

Animating how Adaline classification works by minimizing cost. Showing comparison of three kinds of gradient descent.

Generalized local search tool

MATLAB implementation of gradient descent to estimate the Lipschitz constant, with graph construction and slit-based geometric visualization.

Practice on ML Specialization by Deeplearning.ai

A scalar-valued autograd engine and neural network library built entirely from scratch in Python — no PyTorch, no TensorFlow, just pure math.

Rust implementation of the Adaline artificial neural network algorithm for educational purposes.

Linear regression and Normal equation implementation of predicting the life expectancies in different countries.

Linear Regression implemented from scratch using Gradient Descent in Python

Uses Matrix factorisation on a sparse matrix to predict the missing values of rating of movies by users using stochastic and batch gradient descent.

This repository contains numpy implementations of different ML Algorithms

Multiclass Logistic, Classification Pipeline, Cross Validation, Gradient Descent, Regularization

Predicting House Sale Prices with Machine Learning

Step-by-Step Guide to an Optimization Problem Solver in Scala