Intro to Machine Learning & Neural Networks. How Do They Work?

In this lesson, we will discuss machine learning and neural networks. We will learn about the overall topic of artificial intelligence (AI), with machine learning (ML) being a subset of AI. Neural networks are then a subset of machine learning. Neural networks are a technique where a computer network performs a task without human intervention and trains and improves over time. Applications are in artificial general intelligence, finance, speech recognition, image recognition, self driving cars, and more. We will discuss the networks weights and biases and learn how a real neural network operates and how it is trained. More Lessons: http://www.MathAndScience.com Twitter: https://twitter.com/JasonGibsonMath 00:00:00 Introduction 00:01:23 Applications of Machine Learning 00:10:40 Difference Between AI, ML, & NNs 00:15:36 NNs Inspired by the Brain 00:20:38 What is a Model? 00:25:34 Training Methods 00:31:38 Neural Network Architecture 00:39:22 Input and Output Layers 00:48:00 Neuron Connections 00:57:08 Review of Functions 01:05:52 Neuron Weights and Biases 01:09:37 Writing Neuron Equations 01:19:34 Equations in Matrix Form 01:31:33 How to Train NNs? 01:37:00 The Loss Function