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

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Corrections:
23:45 The sum should go from 1 to *d*

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Animation framework from @3Blue1Brown: https://github.com/3b1b/manim