A complete neural network built entirely in x86 assembly language that learns to recognize handwritten digits from the MNIST dataset. No frameworks, no high-level languages - just pure assembly - ~7× faster than NumPy

Deep Learning Framework only using numpy: Linear, Convolution, Flatten, Max and Mean Pooling layers, activation functions, Softmax, MSE and Cross Entropy. Useful functions: train, save an load a model to deploy it, get nb of parameters, draw learning curves

📖 ⟪밑바닥부터 시작하는 딥러닝2⟫ 스터디 기록 저장소

A coding exercises with numpy and pytest for Deep Learning From Scratch

A lightweight, deep learning library written in pure Python

Neural Network algorithms, concepts and application developed from scratch in python using just numpy, scipy and matplotlib libraries.

밑바닥부터 시작하는 딥러닝 스터디 요약 저장소 (HW)

A fully vectorized Deep Neural Network (DNN) implementation built from scratch using only NumPy - no deep learning frameworks involved. Covers forward/backward propagation, activation functions, modular architecture, and training with different optimizers - a hands-on deep dive into the fundamentals of deep learning.

MNIST Handwritten Digits Classifier From Scratch In C++