Machine Learning, Animated

1. Auflage 2023
ISBN: 978-1-032-46214-1
Verlag: Chapman and Hall/CRC

Buch, Englisch, 464 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 1064 g

Reihe: Chapman & Hall/CRC Machine Learning & Pattern Recognition

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

Zielgruppe

Academic, Adult education, and General

Autoren/Hrsg.

Liu, Mark

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Spiele-Programmierung, Rendering, Animation

Inhaltsverzeichnis

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

Bibliography

Über Autor(innen)

Mark H. Liu is Associate Professor of Finance, (Founding) Director of MS Finance Program, University of Kentucky.
Mark is currently the director of Master of Science in Finance program at the University of Kentucky, U.S.A. He is also an associate professor of finance with tenure at the University of Kentucky.
He obtained his Ph.D. in finance from Boston College in 2004 and his M.A. in economics from Western University in Canada in 1998. His research interest is in machine learning and corporate finance. He has published his research in top finance journals such as Journal of Financial Economics, Journal of Financial and Quantitative Analysis, Journal of Corporate Finance, and Review of Corporate Finance Studies.
Dr. Mark Liu has run Python workshops for master students at the University of Kentucky in the last few years. He has incorporated Python in his teaching. In particular, he is now teaching a Python Predictive Analytics course to graduate students.
As the director of the MS Finance program, Mark has seen first-hand the high demand for machine learning skills in all industries. He has interacted with executives and recruiters from hundreds of companies, who in recent years have put an increasing emphasis on the importance of incorporating machine learning and data analytics skills in all business fields.