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

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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

Summary

In this chapter, we prepared key information to create the input dialog of a chatbot. Using the weights of an RBM as features constituted the first step. We saw that we could use neural networks to extract features from datasets and represent them using the optimized weights.

Processing the likes/dislikes of a movie viewer reveals the features of the movies that, in turn, provide a mental representation of a marketing segment.

PCA chained to an RBM will generate a vector space that can be viewed in TensorBoard Embedding Projector in a few clicks.

Once TensorBoard was set up, we analyzed the statistics to understand the marketing segment the dataset originated from. By listing the points per feature, we found the main features that drove this marketing segment.

Having discovered some of the key features of the marketing segment we were analyzing, we can now move on to the next chapter and start building a chatbot for the viewers. At the same time,...