In this video we go through the mathematics of the widely used Softmax Layer. We then proceed to implement the layer based on the code we wrote in last videos. 😺 GitHub: https://github.com/TheIndependentCode/Neural-Network EIDT: I decided to deactivate my @CodeIndependent Twitter account. Instead I'll use my personal account to tweet about upcoming videos :) 🐦 Twitter: https://twitter.com/omar_aflak Chapters: 00:00 Introduction 00:30 Forward 01:31 Forward Code 02:14 Backward 05:47 Backward Code 06:21 Conclusion

In this video I show you a trick to divide by two the degree of some special sets of polynomials: the symmetrical coefficients polynomial. 😺 GitHub: https://github.com/TheIndependentCode/Neural-Network 🐦 Twitter: https://twitter.com/omar_aflak Chapters: 00:00 The method 01:52 Degree 6 03:00 Generalisation 04:28 Proof 05:28 Summary

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

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 📖 Article: https://omaraflak.github.io/articles/neural-network.html 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