Machine Learning, Animated: (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

Table of Contents:
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

About the Author :
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.