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

Introducing and building an RBM

RBMs are random and undirected graph models generally built with a visible and a hidden layer. They were used in a Netflix competition to predict future user behavior. The goal here is not to predict what a viewer will do but establish who the viewer is and store the data in a viewer's profile-structured mind dataset. The input data represents the features to be trained to learn about viewer X. Each column represents a feature of X's potential personality and tastes. Each line represents the features of a movie that X has watched. The following code (and this section) is in RBM_01.py:

np.array([[1,1,0,0,1,1],
         [1,1,0,1,1,0],
         [1,1,1,0,0,1],
         [1,1,0,1,1,0],
         [1,1,0,0,1,0],
         [1,1,1,0,1,0]])

The goal of this RBM is to define a profile of X by computing the features of the movies watched. The input data could also be images, words, and other forms of data, as in any neural network...