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Python Deep Learning

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

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Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781837638505
Length 362 pages
Edition 3rd Edition
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Author (1):
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Ivan Vasilev Ivan Vasilev
Author Profile Icon Ivan Vasilev
Ivan Vasilev
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Table of Contents (17) Chapters Close

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 16. Other Books You May Enjoy

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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:msub><mml:mrow><mml:mi>a</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>a</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:mo>…</mml:mo><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>a</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:mi>b</mml:mi><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:math>.

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 (<math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mover><mi>x</mi><mo stretchy="true">→</mo></mover></mrow></math>) or in bold (x), but we’ll mostly use the bold notation throughout the book. The following is an example of a vector:

<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math" display="block"><mml:mi mathvariant="bold">x</mml:mi><mml:mo>=</mml:mo><mml:mover accent="true"><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mo>→</mml:mo></mml:mover><mml:mo>=</mml:mo><mml:mfenced open="[" close="]" separators="|"><mml:mrow><mml:msub><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>…</mml:mo><mml:msub><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:math>

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...

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