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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:   / 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

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