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
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22
Index

Chapter 14 – Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA)

  1. RBMs are based on directed graphs. (Yes | No)

    No. RBM graphs are undirected, unsupervised, and memoryless, and the decision-making is based on random calculations.

  2. The hidden units of an RBM are generally connected to one another. (Yes | No)

    No. The hidden units of an RBM are not generally connected to each other.

  3. Random sampling is not used in an RBM. (Yes | No)

    No. False. Gibbs random sampling is frequently applied to RBMs.

  4. PCA transforms data into higher dimensions. (Yes | No)

    Yes. The whole point of PCA is to transform data into a lower number of dimensions in higher abstraction dimensions (key dimensions isolated) to find the principal component (highest eigenvalue of a covariance matrix), then the second highest, down to the lowest values.

  5. In a covariance...