Book Image

Artificial Intelligence By Example - Second Edition

By : Denis Rothman
Book Image

Artificial Intelligence By Example - Second Edition

By: Denis Rothman

Overview of this book

AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing. By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions.
Table of Contents (23 chapters)
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)