Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Artificial Intelligence By Example

You're reading from   Artificial Intelligence By Example Acquire advanced AI, machine learning, and deep learning design skills

Arrow left icon
Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781839211539
Length 578 pages
Edition 2nd Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
Arrow right icon
View More author details
Toc

Table of Contents (23) Chapters Close

Preface 1. Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning 2. Building a Reward Matrix – Designing Your Datasets FREE CHAPTER 3. Machine Intelligence – Evaluation Functions and Numerical Convergence 4. Optimizing Your Solutions with K-Means Clustering 5. How to Use Decision Trees to Enhance K-Means Clustering 6. Innovating AI with Google Translate 7. Optimizing Blockchains with Naive Bayes 8. Solving the XOR Problem with a Feedforward Neural Network 9. Abstract Image Classification with Convolutional Neural Networks (CNNs) 10. Conceptual Representation Learning 11. Combining Reinforcement Learning and Deep Learning 12. AI and the Internet of Things (IoT) 13. Visualizing Networks with TensorFlow 2.x and TensorBoard 14. Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA) 15. Setting Up a Cognitive NLP UI/CUI Chatbot 16. Improving the Emotional Intelligence Deficiencies of Chatbots 17. Genetic Algorithms in Hybrid Neural Networks 18. Neuromorphic Computing 19. Quantum Computing 20. Answers to the Questions 21. Other Books You May Enjoy
22. Index

Questions

The answers to the questions are in Appendix B, with further explanations:

  1. Is reinforcement learning memoryless? (Yes | No)
  2. Does reinforcement learning use stochastic (random) functions? (Yes | No)
  3. Is MDP based on a rule base? (Yes | No)
  4. Is the Q function based on the MDP? (Yes | No)
  5. Is mathematics essential to AI? (Yes | No)
  6. Can the Bellman-MDP process in this chapter apply to many problems? (Yes | No)
  7. Is it impossible for a machine learning program to create another program by itself? (Yes | No)
  8. Is a consultant required to enter business rules in a reinforcement learning program? (Yes | No)
  9. Is reinforcement learning supervised or unsupervised? (Supervised | Unsupervised)
  10. Can Q-learning run without a reward matrix? (Yes | No)
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image