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 5 – How to Use Decision Trees to Enhance K-Means Clustering

The questions will focus on the hyperparameters.

  1. The number of k clusters is not that important. (Yes | No)

    The answer is no. The number of clusters requires careful selection, possibly a trial-and-error approach. Each project will lead to different clusters.

  2. Mini-batches and batches contain the same amount of data. (Yes | No)

    The answer is no. "Batch" generally refers to the dataset, and "mini-batch" represents a "subset" of data.

  3. K-means can run without mini-batches. (Yes | No)

    The answer is yes, and no. If the volume of data remains small, then the training epochs can run on the whole dataset. If the data volume exceeds a reasonable amount of computer power (CPU or GPU), mini-batches must be created to optimize training computation.

  4. Must centroids be optimized for result acceptance? (Yes | No) ...