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Advanced Deep Learning with Python

You're reading from   Advanced Deep Learning with Python Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789956177
Length 468 pages
Edition 1st 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. Section 1: Core Concepts FREE CHAPTER
2. The Nuts and Bolts of Neural Networks 3. Section 2: Computer Vision
4. Understanding Convolutional Networks 5. Advanced Convolutional Networks 6. Object Detection and Image Segmentation 7. Generative Models 8. Section 3: Natural Language and Sequence Processing
9. Language Modeling 10. Understanding Recurrent Networks 11. Sequence-to-Sequence Models and Attention 12. Section 4: A Look to the Future
13. Emerging Neural Network Designs 14. Meta Learning 15. Deep Learning for Autonomous Vehicles 16. Other Books You May Enjoy

The mathematical apparatus of NNs

In the next few sections, we'll discuss the mathematical branches related to NNs. Once we've done this, we'll connect them to NNs themselves.

Linear algebra

Linear algebra deals with linear equations such as and linear transformations (or linear functions) and their representations, such as matrices and vectors.

Linear algebra identifies the following mathematical objects:

  • Scalars: A single number.
  • Vectors: A one-dimensional array of numbers (or components). Each component of the array has an index. In literature, we will see vectors denoted either with a superscript arrow () or in bold (x). The following is an example of a vector:
Throughout this book, we'll mostly...
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