implementation of neural network from scratch only using numpy (Conv, Fc, Maxpool, optimizers and activation functions)

An python implementation of RNN (without deep learning framework)

백준 문제를 풀고 코드 따로 저장 없이 Notion에 바로 커밋하기!

🧠 Collection of neural network implementations from scratch. Clean PyTorch implementations with educational comments and ready training loops.

NanoGPT is a lightweight GPT-style language model designed for text generation. It supports training on custom datasets, pretrained model inference, and minimal dependencies for efficient experimentation.

Implementation of Java, C, C#, and C++'s switch statement.

A Handwritten Number Recognition System built from scratch using Deep Learning from Scratch. The model is trained on digit images and can classify handwritten numbers with high accuracy.

A Python-based command-line tool developed as part of a research project on Machine Learning and IoT. It utilizes a custom implementation of the TF-IDF algorithm to provide interactive and concise three-point answers to IoT-related queries.

KPCA and LDA implementations.

Disclaimer: The content of the repository is based on the tutorial made by Thibault Polge. My goal is just to learn more about Git. Every implementation was done NOT just by copying and pasting, but by trying to understand each line and each command that was being typed.

Python implementation of the neural networks without using any libraries from scratch, for prediction using the pre-trained weights

This project is a complete, from-scratch implementation of the Transformer architecture as described in the "Attention is All You Need" paper.