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 from input pixels to trained weights.
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.
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++
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