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. Can the perceptron alone solve the XOR problem? (Yes | No)
  2. Is the XOR function linearly non-separable? (Yes | No)
  3. One of the main goals of layers in a neural network is classification. (Yes | No)
  4. Is deep learning the only way to classify data? (Yes | No)
  5. A cost function shows the increase in the cost of a neural network. (Yes | No)
  6. Can simple arithmetic be enough to optimize a cost function? (Yes | No)
  7. A feedforward network requires inputs, layers, and an output. (Yes | No)
  8. A feedforward network always requires training with backpropagation. (Yes | No)
  9. In real-life applications, solutions are only found by following existing theories. (Yes | No)