Building a Convolutional Neural Network from Scratch with Python and Mathematics

In this video we'll create a Convolutional Neural Network (or CNN), from scratch in Python. We'll go fully through the mathematics of that layer and then implement it. We'll also implement the Reshape Layer, the Binary Cross Entropy Loss, and the Sigmoid Activation. Finally, we'll use all these objects to make a neural network capable of classifying hand written digits from the MNIST dataset. 😺 GitHub: https://github.com/TheIndependentCode/Neural-Network 🐦 Twitter: https://twitter.com/omar_aflak Chapters: 00:00 Intro 00:33 Video Content 01:26 Convolution & Correlation 03:24 Valid Correlation 03:43 Full Correlation 04:35 Convolutional Layer - Forward 13:04 Convolutional Layer - Backward Overview 13:53 Convolutional Layer - Backward Kernel 18:14 Convolutional Layer - Backward Bias 20:06 Convolutional Layer - Backward Input 27:27 Reshape Layer 27:54 Binary Cross Entropy Loss 29:50 Sigmoid Activation 30:37 MNIST ==== Corrections: 23:45 The sum should go from 1 to *d* ==== Animation framework from @3Blue1Brown: https://github.com/3b1b/manim