A from-scratch implementation of a feedforward neural network in C# (.NET 8) without using any machine learning frameworks.

A convolutional neural network (CNN) built from scratch using only NumPy to classify handwritten digits from the MNIST dataset.

Educational, from-scratch implementation of a LLaMA-style LLM using PyTorch to explore Transformer architecture fundamentals.

An OS which is all about learning!

Implementation of KNN and Gaussian Naive-Bayes algorithms to classify phishing URLs. Built from scratch and compared with scikit-learn versions.

This project demonstrates how to build and train a feedforward neural network from scratch using only NumPy, without any high-level deep learning libraries like TensorFlow or PyTorch. The model is trained on the MNIST digit classification dataset and achieves competitive accuracy.

"Learn Linear Regression: A Python implementation from scratch with dataset generation and visualization" as it's both informative and engaging.

LSTM implemented from scratch and with PyTorch's nn.LSTM, trained using PyTorch Lightning on a toy stock prediction task. Educational and beginner-friendly.

Minimal GPT implementation from scratch using PyTorch — trains a character-level transformer on the Tiny Shakespeare dataset to demonstrate core LLM concepts.