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 10 – Conceptual Representation Learning

  1. The curse of dimensionality leads to reducing dimensions and features in machine learning algorithms. (Yes | No)

    Yes. The volume of data and features makes it necessary to extract the main features of an observed event (an image, sound, and words) to make sense of it.

    Overfitting and underfitting apply to dimensionality reduction as well. Reducing the features until the system works in a lab (overfitting) might lead to nowhere once the application faces real-life data. Trying to use all the features might lead to underfitting because the application solves no problem at all.

    Regularization applies not just to data but to every aspect of a project.

  2. Transfer learning determines the profitability of a project. (Yes | No)

    Yes, if an application of an AI model in itself was unprofitable the first time, but could generate profit if used for a similar type of learning. Reusing some...