Liu - Machine Learning, Animated

Machine Learning, Animated

Medium: Buch

ISBN: 978-1-032-46214-1

Verlag: Chapman and Hall/CRC

Erscheinungstermin: 31.10.2023

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The release of ChatGPT has kicked off an arms race in Machine Learning (ML), however ML has also been described as a black box and very hard to understand. Machine Learning, Animated eases you into basic ML concepts and summarizes the learning process in three words: initialize, adjust and repeat. This is illustrated step by step with animation to show how machines learn: from initial parameter values to adjusting each step, to the final converged parameters and predictions.

This book teaches readers to create their own neural networks with dense and convolutional layers, and use them to make binary and multi-category classifications. Readers will learn how to build deep learning game strategies and combine this with reinforcement learning, witnessing AI achieve super-human performance in Atari games such as Breakout, Space Invaders, Seaquest and Beam Rider.

Written in a clear and concise style, illustrated with animations and images, this book is particularly appealing to readers with no background in computer science, mathematics or statistics.

Access the book's repository at: https://github.com/markhliu/MLA

Produkteigenschaften

Artikelnummer: 9781032462141
Medium: Buch
ISBN: 978-1-032-46214-1
Verlag: Chapman and Hall/CRC
Erscheinungstermin: 31.10.2023
Sprache(n): Englisch
Auflage: 1. Auflage 2023
Serie: Chapman & Hall/CRC Machine Learning & Pattern Recognition
Produktform: Gebunden
Gewicht: 1064 g
Seiten: 464
Format (B x H x T): 183 x 260 x 29 mm
Ausgabetyp: Kein, Unbekannt

Themen

Autoren

List of Figures

Preface

Section I Installing Python and Learning Animations

1. Installing Anaconda and Jupyter Notebook

2. Creating Animations

Section II Machine Learning Basics

3. Machine Learning: An Overview

4. Gradient Descent - Where the Magic Happens

5. Introduction to Neural Networks

6. Activation Functions

Section III Binary and Multi-Category Classifications

7. Binary Classifications

8. Convolutional Neural Networks

9. Multi-Category Image Classifications

Section IV Developing Deep Learning Game Strategies

10. Deep Learning Game Strategies

11. Deep Learning in the Cart Pole Game

12. Deep Learning in Multi-Player Games

13. Deep Learning in Connect Four

Section V Reinforcement Learning

14. Introduction to Reinforcement Learning

15. Q-Learning with Continuous States

16. Solving Real-World Problems with Machine Learning

Section VI Deep Reinforcement Learning

17. Deep Q-Learning

18. Policy-Based Deep Reinforcement Learning

19. The Policy Gradient Method in Breakout

20. Double Deep Q-Learning

21. Space Invaders with Double Deep Q-Learning

22. Scaling Up Double Deep Q-Learning

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