You're reading from Artificial Intelligence By Example Acquire advanced AI, machine learning, and deep learning design skills

Product type Paperback

Published in Feb 2020

Publisher Packt

ISBN-13 9781839211539

Length 578 pages

Edition 2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Author (1):

Denis Rothman

View More author details

Table of Contents (23) Chapters

Preface

1. Getting Started with Next-Generation Artificial Intelligence through Reinforcement Learning

2. Building a Reward Matrix – Designing Your Datasets FREE CHAPTER

3. Machine Intelligence – Evaluation Functions and Numerical Convergence

4. Optimizing Your Solutions with K-Means Clustering

5. How to Use Decision Trees to Enhance K-Means Clustering

6. Innovating AI with Google Translate

7. Optimizing Blockchains with Naive Bayes

8. Solving the XOR Problem with a Feedforward Neural Network

9. Abstract Image Classification with Convolutional Neural Networks (CNNs)

10. Conceptual Representation Learning

11. Combining Reinforcement Learning and Deep Learning

12. AI and the Internet of Things (IoT)

13. Visualizing Networks with TensorFlow 2.x and TensorBoard

14. Preparing the Input of Chatbots with Restricted Boltzmann Machines (RBMs) and Principal Component Analysis (PCA)

15. Setting Up a Cognitive NLP UI/CUI Chatbot

16. Improving the Emotional Intelligence Deficiencies of Chatbots

17. Genetic Algorithms in Hybrid Neural Networks

18. Neuromorphic Computing

19. Quantum Computing

20. Answers to the Questions

21. Other Books You May Enjoy

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...