Convolutional Neural Network from Scratch Mathematics amp Python Code

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http://youtube.com/watch?v=Lakz2MoHy6o

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... • 🐦 Twitter: / 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

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