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 - ~5.3× faster than NumPy

Data science teaching materials

A neural network library written from scratch in Rust along with a web-based application for building + training neural networks + visualizing their outputs

Detailed python notes & code for lectures and exercises of Andrej Karpathy's course "Neural Networks: Zero to Hero." The course is focused on building neural networks from scratch.

🤖 A TypeScript version of karpathy/micrograd — a tiny scalar-valued autograd engine and a neural net on top of it

An Open Convolutional Neural Network Framework in C++ From Scratch

Neural Networks from Scratch in Python crafted for utilization as teaching resources in graduate courses (Deep Learning, Deep Learning for Computer Vision) delivered by Minh-Chien Trinh at Jeonbuk National University.

Deep learning library in python from scratch

Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity

A step-by-step walkthrough of the inner workings of a simple neural network. The goal is to demystify the calculations behind neural networks by breaking them down into understandable components, including forward propagation, backpropagation, gradient calculations, and parameter updates.

Learn machine learning the hard way

My first ML sandbox

Implementation of feedforward-backpropagated Neural Network from Scratch

deep learning from scratch. uses numpy/cupy, trains in GPU, follows pytorch API

A causal inference engine for deep learning training that provides structured explanations of neural network training failures. Understand why your model failed during training through semantic analysis and abductive reasoning, not raw tensor inspection.

Neural Networks Fundamentals with Python – implementing neural networks from scratch

Lightweight, easy to use, micro neural network framework written in Rust w/ no python dependencies

Let's build Neural Networks from scratch.

Matrix-Vector Library Designed for Neural Network Construction. cuda (gpu) support, openmp (multithreaded cpu) support, partial support of BLAS, expression template based implementation PTX code generation identical to hand written kernels, and support for auto-differentiation

Neural nets for high accuracy multivariable nonlinear regression.

Learn to build neural networks from scratch, simply. No autograd, no deep learning libraries - just numpy.

Artificial Neural Networks (ANNs) Projects

This is my first Deep Learning project, which is a MNIST hand-written digits classifier. The model is implemented completely from scratch WITHOUT using any prebuilt optimization like Tensorflow or Pytorch. Tensorflow is imported only to load the MNIST data set. This model also uses 2 hidden layers with Adaptive Moment Optimization (Adam) and Drop-out regularization.

A set of Jupyter notebooks implementing simple neural networks described in Michael Nielsen's book.

XOR gate which predicts the output using Neural Network :fire:

Deep Learning course, Sharif University of Technology, Dr. Soleymani, Spring 2024

This repository contains an implementation of a neural network from scratch using only NumPy, a fundamental library for numerical computing in Python. The neural network is designed to perform tasks such as classification, regression, or any other supervised learning problem.

Pure Python Simple Neural Network (SNN) library

To understand neural networks thoroughly I implemented them from scratch in C++. This is the source code for the same.

Neural Network with VHDL and matlab