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Artificial Intelligence By Example

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

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781839211539
Length 578 pages
Edition 2nd Edition
Languages
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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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

  1. A cell contains 42 chromosomes. (Yes | No)
  2. A genetic algorithm is deterministic, not random. (Yes | No)
  3. An evolutionary algorithm means that the program code evolves. (Yes | No)
  4. It is best for a child to have the same genes as one of the parents even after many generations. (Yes | No)
  5. Diversity makes the gene sets weaker. (Yes | No)
  6. Building a neural network only takes a few lines, and the architecture always works. (Yes | No)
  7. Building a neural network with a genetic algorithm can help optimize the architecture of the layers. (Yes | No)
  8. Hybrid neural networks are useless since deep learning will constantly progress. (Yes | No)
  9. Would you trust a genetic algorithm to make decisions for you? (Yes | No)
  10. Would you trust a hybrid neural network to optimize the architecture of your network? (Yes | No)
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