from-scratch-in-python

Companion code for Machine Learning From Scratch — 10 core ML algorithms built from scratch with NumPy, compared with Scikit-learn and PyTorch.

Implementation of a small GPT-style transformer from scratch in PyTorch. Learn how Large Language Models work by building, training, generating text, and visualizing attention.

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

"One Hidden Layer Neural Network" from Scratch

A compact collection of classic machine learning algorithms implemented from scratch in Python for learning and experimentation.

This repository implements a vector search solution based on image and text embeddings. Users can search for similar products using an image or a textual description.

"Deep Neural Network" from Scratch

A brand new programming language designed for developers with diverse coding interests!

"CNN" from Scratch

Includes the codes and report for Take Home Exam 2 of the CENG483 course (Introduction to Computer Vision). The purpose of this THE is to gain insight related to harris interest point detection.

"Logistic Regression" from Scratch

从零手撕大模型整个生命周期的核心算子（如tokenization、RMSNorm、RoPE和LoRA），附带对应论文的阅读详解和笔记整理！！

A STRONG CHESS BOT WITH A GUI INTERFACE MADE PURELY IN PYTHON FROM SCRATCH

Building a Large Language Model from scratch for deep understanding — inspired by Sebastian Raschka’s book, implemented entirely by hand.

ML algorithms implemented from scratch in Python, with small projects for better understanding.

Includes the codes and report for Take Home Exam 1 of the CENG483 course (Introduction to Computer Vision). The purpose of the THE is familiarize ourselves with the concept of various types of histogram.

Write the K Nearest Neighbors classifier from scratch in Python. Test it on email spam classification dataset.

Interactive CLI-based KNN implementation from scratch with data processing, statistics, visualization, feature normalization, and accuracy computation functionalities.

A browser from scratch written in Python, with the Arcade and Pyglet modules. Code is based on browser.engineering's tutorial.

Text-LLM + Edge-VLM systems core — hand-coded from scratch: custom tokenizer, causal transformer (RoPE/GQA/SwiGLU), KV-cache, LoRA, pruning, AWQ INT4 quantization, ONNX export, Perceiver vision bridge, PagedAttention, speculative decoding, and a native C++ inference runner.