You're reading from Python Deep Learning Understand how deep neural networks work and apply them to real-world tasks

Product type Paperback

Published in Nov 2023

Publisher Packt

ISBN-13 9781837638505

Length 362 pages

Edition 3rd Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Author (1):

Vasilev

View More author details

Table of Contents (17) Chapters

Preface

1. Part 1:Introduction to Neural Networks

2. Chapter 1: Machine Learning – an Introduction FREE CHAPTER

3. Chapter 2: Neural Networks

4. Chapter 3: Deep Learning Fundamentals

5. Part 2: Deep Neural Networks for Computer Vision

6. Chapter 4: Computer Vision with Convolutional Networks

7. Chapter 5: Advanced Computer Vision Applications

8. Part 3: Natural Language Processing and Transformers

9. Chapter 6: Natural Language Processing and Recurrent Neural Networks

10. Chapter 7: The Attention Mechanism and Transformers

11. Chapter 8: Exploring Large Language Models in Depth

12. Chapter 9: Advanced Applications of Large Language Models

13. Part 4: Developing and Deploying Deep Neural Networks

14. Chapter 10: Machine Learning Operations (MLOps)

15. Index

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The math of NNs

In the following few sections, we’ll discuss the mathematical principles of NNs. This way, we’ll be able to explain NNs through these very principles in a fundamental and structured way.

Linear algebra

Linear algebra deals with objects such as vectors and matrices, linear transformations, and linear equations such as .

Linear algebra identifies the following mathematical objects:

Scalar: A single number.
Vector: A one-dimensional array of numbers (also known as components or scalars), where each element has an index. We can denote vectors either with a superscript arrow () or in bold (x), but we’ll mostly use the bold notation throughout the book. The following is an example of a vector:

We can represent a n-dimensional vector as the coordinates of a point in an n-dimensional Euclidean space, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:msup><mml:mrow><mml:mi mathvariant="double-struck">R</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:math> . Think of Euclidean space as a coordinate system – the vector starts at the center of that system, and each of the...