UID:
almahu_9949385866502882
Umfang:
1 online resource (xii, 344 pages)
Ausgabe:
First edition.
ISBN:
9781003214892
,
1003214894
,
9781000577396
,
1000577392
,
9781000577365
,
1000577368
Inhalt:
Artificial Intelligence: An Introduction for the Inquisitive Reader guides readers through the history and development of AI, from its early mathematical beginnings through to the exciting possibilities of its potential future applications. To make this journey as accessible as possible, the authors build their narrative around accounts of some of the more popular and well-known demonstrations of artificial intelligence including Deep Blue, AlphaGo and even Texas Hold'em, followed by their historical background, so that AI can be seen as a natural development of mathematics and computer science. As the book moves forward, more technical descriptions are presented at a pace that should be suitable for all levels of readers, gradually building a broad and reasonably deep understanding and appreciation for the basic mathematics, physics, and computer science that is rapidly developing artificial intelligence as it is today. Features: Only mathematical prerequisite is an elementary knowledge of calculus Accessible to anyone with an interest in AI and its mathematics and computer science Suitable as a supplementary reading for a course in AI or the History of Mathematics and Computer Science in regard to artificial intelligence.
Anmerkung:
PART I: The Arrival of AI in the Human World. 1. Game-Playing. 2. Working Machines. 3. Intelligence. 4. The AI Singularity. PART II: The Artificial Intelligence Infrastructure. 5. Hardware. 6. Software. 7. Computer Communications. 8. Open Source Software. PART III: From Top to Bottom. 9. Top-Down Artificial Intelligence. 10. Bottom-Up Artificial Intelligence. 11. Machine Learning Modeling. 12. Markov Chain Monte Carlo Simulation. PART IV: Structure and Operation. 13. Artificial Neural Networks. 14. Pattern Recognition. 15. Parameterization. 16. Gradient Descent. 17. Backpropagation. 18. Convolutional Neural Networks. PART V: Progression. 19. The Cross-Entropy Cost Function. 20. Hyperparameterization. 21. Big Data. 22. Massively Parallel Processing. PART VI: Powers of Prediction. 23. Predictive Analytics. 24. Restricted Boltzmann Machine. 25. Latent Factors in Collaborative Filtering. 26. Support Vector Machines. 27. Reinforcement Learning. 28. AlphaGo and AlphaStar. 29. Game Theory. PART VII: Natural Language Processing. 30. Top-Down Speech Recognition. 31. Bottom-Up Speech Recognition. 32. Speech Synthesis. PART VIII: The Robotworld. 33. Robots at Work. 34. The Robot Millennial. 35. The Robot Future
Sprache:
Englisch
Schlagwort(e):
Electronic books.
DOI:
10.1201/9781003214892
URL:
https://www.taylorfrancis.com/books/9781003214892
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