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Hands-On Artificial Intelligence for Beginners

You're reading from   Hands-On Artificial Intelligence for Beginners An introduction to AI concepts, algorithms, and their implementation

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781788991063
Length 362 pages
Edition 1st Edition
Languages
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Authors (2):
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David Dindi David Dindi
Author Profile Icon David Dindi
David Dindi
Patrick D. Smith Patrick D. Smith
Author Profile Icon Patrick D. Smith
Patrick D. Smith
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Table of Contents (15) Chapters Close

Preface 1. The History of AI 2. Machine Learning Basics FREE CHAPTER 3. Platforms and Other Essentials 4. Your First Artificial Neural Networks 5. Convolutional Neural Networks 6. Recurrent Neural Networks 7. Generative Models 8. Reinforcement Learning 9. Deep Learning for Intelligent Agents 10. Deep Learning for Game Playing 11. Deep Learning for Finance 12. Deep Learning for Robotics 13. Deploying and Maintaining AI Applications 14. Other Books You May Enjoy

Applied math basics

When we talk about mathematics as related to deep learning and AI, we're often talking about linear algebra. Linear algebra is a branch of continuous mathematics that involves the study of vector space and operations performed in vector space. If you remember back to grade-school algebra, algebra in general deals with unknown variables. With linear algebra, we're extending this study into linear systems that have an arbitrary number of dimensions, which is what makes this a form of continuous mathematics.

AI relies on the basic building block of the tensor. Within AI, these mathematical objects store information throughout ANNs that allow them to operate; they are data structures that are utilized throughout AI. As we will see, a tensor has a rank, which essentially tells us about the indices of the data (how many rows and columns the data has).

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You have been reading a chapter from
Hands-On Artificial Intelligence for Beginners
Published in: Oct 2018
Publisher: Packt
ISBN-13: 9781788991063
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