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 8 – Solving the XOR Problem with a Feedforward Neural Network

  1. Can the perceptron alone solve the XOR problem? (Yes | No)

    Yes. The answer would have been no in 1969. A neural network, or some other mathematical process, is necessary to solve this problem. For the record, this is a common problem for electric circuits that function with "feedforward" electricity, and was solved long ago.

  2. Is the XOR function linearly non-separable? (Yes | No)

    The answer is no if you use a single neuron, and yes if you use a hidden layer with at least two neurons. That is a major problem to address in deep learning. If you cannot separate the features of a face, for example, in a picture, recognizing that face will prove difficult. Imagine a picture with one half of the face in shadow and the other half in bright sunlight. Since the eye and features of one half are in shadow, a poor deep learning program might only capture half...