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Machine Learning, Animated

Medium: Buch
ISBN: 978-1-032-46214-1
Verlag: Chapman and Hall/CRC
Erscheinungstermin: 31.10.2023
Lieferfrist: bis zu 10 Tage

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


Produkteigenschaften


  • Artikelnummer: 9781032462141
  • Medium: Buch
  • ISBN: 978-1-032-46214-1
  • Verlag: Chapman and Hall/CRC
  • Erscheinungstermin: 31.10.2023
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2023
  • Serie: Chapman & Hall/CRC Machine Learning & Pattern Recognition
  • Produktform: Gebunden, HC gerader Rücken kaschiert
  • Gewicht: 1064 g
  • Seiten: 464
  • Format (B x H x T): 183 x 260 x 29 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

List of FiguresPrefaceSection I Installing Python and Learning Animations1. Installing Anaconda and Jupyter Notebook2. Creating AnimationsSection II Machine Learning Basics3. Machine Learning: An Overview4. Gradient Descent - Where the Magic Happens5. Introduction to Neural Networks6. Activation FunctionsSection III Binary and Multi-Category Classifications7. Binary Classifications8. Convolutional Neural Networks9. Multi-Category Image ClassificationsSection IV Developing Deep Learning Game Strategies10. Deep Learning Game Strategies11. Deep Learning in the Cart Pole Game12. Deep Learning in Multi-Player Games13. Deep Learning in Connect FourSection V Reinforcement Learning14. Introduction to Reinforcement Learning15. Q-Learning with Continuous States16. Solving Real-World Problems with Machine LearningSection VI Deep Reinforcement Learning17. Deep Q-Learning18. Policy-Based Deep Reinforcement Learning19. The Policy Gradient Method in Breakout20. Double Deep Q-Learning21. Space Invaders with Double Deep Q-Learning22. Scaling Up Double Deep Q-LearningBibliography