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Hands-On Artificial Intelligence for IoT

You're reading from   Hands-On Artificial Intelligence for IoT Expert machine learning and deep learning techniques for developing smarter IoT systems

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781788836067
Length 390 pages
Edition 2nd Edition
Languages
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Author (1):
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Dr. Amita Kapoor Dr. Amita Kapoor
Author Profile Icon Dr. Amita Kapoor
Dr. Amita Kapoor
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Table of Contents (14) Chapters Close

Preface 1. Principles and Foundations of IoT and AI 2. Data Access and Distributed Processing for IoT FREE CHAPTER 3. Machine Learning for IoT 4. Deep Learning for IoT 5. Genetic Algorithms for IoT 6. Reinforcement Learning for IoT 7. Generative Models for IoT 8. Distributed AI for IoT 9. Personal and Home IoT 10. AI for the Industrial IoT 11. AI for Smart Cities IoT 12. Combining It All Together 13. Other Books You May Enjoy

Learning paradigms

ML algorithms can be classified based on the method they use as follows:

  • Probabilistic versus non-probabilistic
  • Modeling versus optimization
  • Supervised versus unsupervised

In this book, we classify our ML algorithms as supervised versus unsupervised. The distinction between these two depends on how the model learns and the type of data that's provided to the model to learn:

  • Supervised learning: Let's say I give you a series and ask you to predict the next element:
(1, 4, 9, 16, 25,...)

You guessed right: the next number will be 36, followed by 49 and so on. This is supervised learning, also called learning by example; you weren't told that the series represents the square of positive integers—you were able to guess it from the five examples provided.

In a similar manner, in supervised learning, the machine learns from example. It&apos...

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