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

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

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

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

Python Toolkit for Uncertainty Quantification

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

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

A series of machine learning and deep learning projects in finance.

Learning about Perceptron and Multi layered perceptron

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

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

Implementing a 4 layer neural to identify digits from a 28x28 grid using just python and numpy

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 proportional to the positive of the gradient, we approach a local maximum of that function; the procedure is then known as gradient ascent.

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CNN, ANN, Python, Matlab

These are the solutions to the programming assigments from Andrew Ng's "Machine Learning" course from Coursera

Python version of Andrew Ng's Machine Learning Course.

Some algorithms which uses neural networks to solve i.e., forwardpropagation , etc.,

Designing Your Own Deep Neural Network

CNN MATLAB implementation (including training and forward propagation) to clasifify the MNIST handwritten numbers.

A minimal neural network built from scratch in NumPy that learns the XOR function, demonstrating forward propagation, backpropagation, and gradient descent without any ML frameworks.

The code of forward propagation , cost function , backpropagation and visualize the hidden layer.

Neural Network using NumPy, V1: Built from scratch. V2: Optimised with hyperparameter search.