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)

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

백준 문제를 풀고 코드 따로 저장 없이 Notion에 바로 커밋하기!

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.

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

KPCA and LDA implementations.

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.

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.

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