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.

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

[williamkeys]

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[aminam]

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[gilbertengler]

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[ashrafkhalil]

I watched hundreds of videos to understand the topic, they all started from an advanced point that's only understandable by professionals. You sir explained the basics very good that a non professional like me (I'm a radiologist) could figure out the basic idea. Thanks so much 👍

[anurasilva]

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[walangpinag]

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[corneillembiya]

Thanks so much for this clarification, please we need more of these...NN, ML and DL

[khalifakhalifa]

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[damirdze]

Man, Sir, you deserve a deep respect for this presentation. You have nailed it to the most important aspects and in manner that people can not only understand it but also be able to make experimental setups in the Matlab. I am writing this because I read about it and listen about it, watched other NN YouTube presentations even tried myself, and I understood the NN to some degree before. However, this is the first video, after I have no questions about it. Does not mean I understand all, it just left me to understand enough to be on my own. Thanks so much.

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[sebon]

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[flowtheory]

Finally, a quick and simple overview to explain the basis of neural network for a computer geek. Thank you!

[ksvozil]

Very nice intro, thank you. As a theoretical physicist trained in quantum mechanics (aka Hilbert spaces) it was easy to follow. And I know how difficult it is to develop such lectures, with the interested but not very experienced student in mind, so thank you again and congratulations!