Build a Multilayer Perceptron Classifier from Scratch with Numpy

This video covers how to build a simple 3-layer feed forward Neural Network from scratch. Here we will implement a Python class that will encapsulate all the logic necessary to build this model. This includes an implementation of backpropagation with numpy. We will then test out our implementation on a simple dataset to ensure it works correctly. The break-down of this video is as follows: Introduction 00:00 Why build a Neural Network from scratch? 00:51 The Neural Network we will build 01:49 Model Implementation 06:05 Test Implementation 20:58 Conclusions 24:22 The best way to keep up-to-date with my video/blog content is to sign up for my monthly Newsletter! Please visit: https://insidelearningmachines.com/newsletter/ to register. This video is based off of an article on my blog. You can find that blog article here: https://insidelearningmachines.com/multilayer_perceptron_classifier/ My blog post on backpropagation can be found here: https://insidelearningmachines.com/understanding_backpropagation/ I also have a blog post covering batch gradient descent: https://insidelearningmachines.com/batch_gradient_descent/ The homepage of my blog is: https://insidelearningmachines.com All code presented here can be found at: https://github.com/insidelearningmachines/Blog/blob/main/Notebook%20XXV%20Multilayer%20Perceptron%20Classifier%20in%20Numpy%20from%20Scratch.ipynb Other social media includes: Twitter: https://twitter.com/inside_machines Facebook: https://www.facebook.com/Inside-Learning-Machines-112215488183517 #machinelearning #datascience #neuralnetworks #insidelearningmachines