Machine Learning, Animated
1st Edition
Published on 31. October 2023
464 pages
978-1-000-96477-6 (ISBN)
Description
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
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
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Adult education
Illustrations
41 Line drawings, color; 4 Halftones, color; 45 Illustrations, color
ISBN-13
978-1-000-96477-6 (9781000964776)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Mark Liu
Machine Learning, Animated
Book
approx. 11/2025
1st Edition
Chapman & Hall/CRC
€143.80
Not yet published
Mark Liu
Machine Learning, Animated
Book
10/2023
1st Edition
Chapman & Hall/CRC
€143.50
Shipment within 10-20 days
Person
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.
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.
Content
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
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
