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

Using the weights of an RBM as feature vectors for PCA

In this section, we will be writing an enhanced version of RBM_01.py. RBM_01.py produces the feature vector of one viewer named X. The goal now is to extract the features of 12,000 viewers, for example, to have a sufficient number of feature vectors for PCA.

In RBM_01.py, viewer X's favorite movies were first provided in a matrix. The goal now is to produce a random sample of 12,000 viewer vectors.

The first task at hand is to create an RBM launcher to run the RBM 12,000 times to simulate a random choice of viewers and their favorite movies, which are the ones the viewer liked. Then, the feature vector of each viewer will be stored.

RBM_launcher.py first imports RBM as rp:

import RBM as rp

The primary goal of RBM_launcher.py is to carry out the basic functions to run RBM. Once RBM is imported, the feature vector's .tsv file is created:

#Create feature files
f=open("features.tsv&quot...