Artificial Intelligence Programming in Python: Exploring the Boundaries of Deep Learning, Symbolic AI, and Knowledge Representation

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Hybrid AI, Python, natural language processing, probability, deep learning, semantic web, linked data

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Artificial Intelligence Programming in Python: Exploring the Boundaries of Deep Learning, Symbolic AI, and Knowledge Representation29 chapters

Begin
  1. Cover Material, Copyright, and License

  2. Preface

  3. Part I - A Brief Introduction to AI and Effectively Using Python

  4. An Introduction to Artificial Intelligence

  5. Python Tips and Techniques

  6. Ethics and Human Values

  7. Part II - Machine Learning and Deep Learning

  8. (Almost) Everything You Really Need To Know About Machine Learning

  9. Tensoflow and Keras Model for Wisconsin Cancer Data Set

  10. Using TensorFlow for Implementing Deep Neural Networks

  11. Generating Images With Deep Learning

  12. Part III - Natural Language Processing

  13. Introduction to Natural Language Processing

  14. Entity Detection

  15. Text Generation

  16. Part IV - Knowledge Representation Using Semantic Web Technologies

  17. Knowledge Representation Using the Neo4J Graph Database

  18. Linked Data and the Semantic Web

  19. Knowledge Graph Creator

  20. Knowledge Graph Navigator

  21. DBPedia Question Answering System Using SparQL and Deep Learning

  22. Part V - GOFAI - Good Old Fashioned Symbolic Artificial Intelligence

  23. Part VI - Hybrid Symbolic and Deep Learning Approaches to Artificial Intelligence

  24. Part VII - Miscellaneous Code and Techniques

  25. Using the Microsoft Bing Search APIs

  26. Responsible Web Scraping

  27. Displaying Graph Data Using PyGraphViz

  28. Introduction to Programming Quantum Computing

  29. Book Wrap Up