The Learning Algorithm for a Multilayer Perceptron Classifier

This video covers the learning algorithm for a simple 3-layer feed forward Neural Network. We will first cover the design of the Neural Network, before describing the learning strategy. It is assumed that the viewer has a basic understanding of linear algebra and calculus. The break-down of this video is as follows: Introduction 00:00 The Neural Network we will build 01:04 Operations inside a Neuron 02:16 The parameters to learn for the Neural Network 05:25 Gradient descent 07:48 Initializing parameters 10:16 Introducing the learning procedure 12:36 Forward pass 14:19 Backward pass 15:54 Update model parameters 24:39 Conclusions 25:34 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. If you're interested to see the algorithm presented here actually implemented into code, check out this video: https://youtu.be/C5Xwt0Jrs_I This video is loosely based off of an article on my blog. The article considers a regression example, whereas here we are working with classification. You can find that blog article here: https://insidelearningmachines.com/how_neural_networks_learn/ The homepage of my blog is: https://insidelearningmachines.com Other social media includes: Twitter: https://twitter.com/inside_machines Facebook: https://www.facebook.com/Inside-Learning-Machines-112215488183517 #machinelearning #datascience #neuralnetworks #insidelearningmachines