Mathematics paper recapitulating the calculus behind a neural network and its back propagation

Implemented Convolutional Neural Network, LSTM Neural Network, and Neural Network From Scratch in Python Language.

Notes & Code to go over "Grokking Deep Learning" Book by Andrew Trask

This repository offers video-based tutorials on Deep Learning concepts along with practical implementations using Python, TensorFlow, and Keras. It is designed for students, educators, and self-learners who want to understand the theory and apply it through hands-on projects.

搭建、深度学习、前向传播、反向传播、梯度下降和模型参数更新、classification、forward-propagation、backward-propagation、gradient descent、python、text classification

Neural Network with functions for forward propagation, error calculation and back propagation is built from scratch and is used to analyse the IRIS dataset.

Fully Connected Neural Network (FCNN) from scratch in python & Notes to aid understanding the workings of neural networks

Code for my youtube video: Neural Network Crash Course, Ep 1

Python Toolkit for Uncertainty Quantification

building a deep neural network with as many layers as you want!

A from-scratch Multilayer Perceptron (MLP) for classifying MNIST handwritten digits. Built without TensorFlow, Keras, or PyTorch, using only basic Python and CuPy.

Artificial Neural Network - Wisconsin Breast Cancer Detection

Learning about Perceptron and Multi layered perceptron

Neural Network from scratch using Python and NumPy, featuring forward/backward propagation and basic optimizers. Perfect for learning deep learning fundamentals.

Este repositorio sirve de apoyo en la asignatura de Redes Neuronales.

Python version of Andrew Ng's Machine Learning Course.

A comparison of fully connected network (forward and backward propagation) implementations.

A highly modular design and implementation of fully-connected feedforward neural network structured on NumPy matrices

Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. To find a local minimum of a function using gradient descent, we take steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point. But if we instead take steps proporti…