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Machine Learning, Animated

 
Edition number: 1
Publisher: Chapman and Hall
Date of Publication:
 
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GBP 74.99
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36 220 HUF (34 495 HUF + 5% VAT)
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Short 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. This book eases you into basic ML concepts and summarises the learning process in three words: initialize, adjust and repeat.

Long 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

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