Neural Network from Scratch | Mathematics & Python Code

In this video we'll see how to create our own Machine Learning library, like Keras, from scratch in Python. The goal is to be able to create various neural network architectures in a lego-fashion way. We'll see how we should architecture the code so that we can create one class per layer. We will go through the mathematics of every layer that we implement, namely the Dense or Fully Connected layer, and the Activation layer. 😺 GitHub: https://github.com/TheIndependentCode/Neural-Network 🐦 Twitter: https://twitter.com/omar_aflak Same content in an article: https://towardsdatascience.com/math-neural-network-from-scratch-in-python-d6da9f29ce65 Chapters: 00:00 Intro 01:09 The plan 01:56 ML Reminder 02:51 Implementation Design 06:40 Base Layer Code 07:55 Dense Layer Forward 10:42 Dense Layer Backward Plan 11:23 Dense Layer Weights Gradient 14:59 Dense Layer Bias Gradient 16:28 Dense Layer Input Gradient 18:22 Dense Layer Code 19:43 Activation Layer Forward 20:46 Activation Layer Input Gradient 22:30 Hyperbolic Tangent 23:24 Mean Squared Error 26:05 XOR Intro 27:04 Linear Separability 27:45 XOR Code 30:32 XOR Decision Boundary ==== Corrections: 17:46 Bottom row of W^t should be w1i, w2i, ..., wji 18:58 dE/dX should be computed before updating weights and biases ==== Animation framework from @3Blue1Brown : https://github.com/3b1b/manim