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

Summary

Building a small neural network from scratch provides a practical view of the elementary properties of a neuron. We saw that a neuron requires an input that can contain many variables. Then, weights are applied to the values with biases. An activation function then transforms the result and produces an output.

Neural networks, even one- or two-layer networks, can provide real-life solutions in a corporate environment. A real-life business case was implemented using complex theory broken down into small functions. Then, these components were assembled to be as minimal and profitable as possible.

It takes talent to break a problem down into elementary parts and find a simple, powerful solution. It requires more effort than just typing hundreds to thousands of lines of code to make things work. A well-thought through algorithm will always be more profitable, and software maintenance will prove more cost-effective.

Customers expect quick-win solutions. Artificial intelligence...