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E-raamat: Machine Learning, Animated

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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. This book eases you into basic ML concepts and summarises the learning process in three words: initialize, adjust and repeat.



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 summarises 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.

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 Classifciations

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. Apply Deep Learning to 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. Solve 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. Scale Up Double Deep Q-Learning

Bibliography

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.
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