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

Summary

In this chapter, we prepared key information to create the input dialog of a chatbot. Using the weights of an RBM as features constituted the first step. We saw that we could use neural networks to extract features from datasets and represent them using the optimized weights.

Processing the likes/dislikes of a movie viewer reveals the features of the movies that, in turn, provide a mental representation of a marketing segment.

PCA chained to an RBM will generate a vector space that can be viewed in TensorBoard Embedding Projector in a few clicks.

Once TensorBoard was set up, we analyzed the statistics to understand the marketing segment the dataset originated from. By listing the points per feature, we found the main features that drove this marketing segment.

Having discovered some of the key features of the marketing segment we were analyzing, we can now move on to the next chapter and start building a chatbot for the viewers. At the same time,...