Packt+ | Advance your knowledge in tech

You're reading from Artificial Intelligence By Example Develop machine intelligence from scratch using real artificial intelligence use cases

Product type Paperback

Published in May 2018

Publisher Packt

ISBN-13 9781788990547

Length 490 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Author (1):

Denis Rothman

View More author details

Table of Contents (24) Chapters

Title Page

Dedication

Packt Upsell

Contributors

Preface

1. Become an Adaptive Thinker FREE CHAPTER

2. Think like a Machine

3. Apply Machine Thinking to a Human Problem

4. Become an Unconventional Innovator

5. Manage the Power of Machine Learning and Deep Learning

6. Don't Get Lost in Techniques – Focus on Optimizing Your Solutions

7. When and How to Use Artificial Intelligence

8. Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies

9. Getting Your Neurons to Work

10. Applying Biomimicking to Artificial Intelligence

11. Conceptual Representation Learning

12. Automated Planning and Scheduling

13. AI and the Internet of Things (IoT)

14. Optimizing Blockchains with AI

15. Cognitive NLP Chatbots

16. Improve the Emotional Intelligence Deficiencies of Chatbots

17. Quantum Computers That Think

1. Answers to the Questions

Index

Human biomimicking

In this section, the difference between neuroscience research on the brain and deep learning research on the mind has led to TensorFlow and TensorBoard. With TensorFlow, our representation of image recognition in a mathematical graph data flow structure has become an extremely efficient model. TensorBoard provides a visual representation of what we are building.

TensorFlow represents how our mind recognizes images (and other elements). TensorBoard enables our minds to visualize that representation and improve it.

TensorFlow, an open source machine learning framework

TensorFlow relies on graph representations of data flow. The graph computes dependencies between operations.

When using Keras, as in Chapter 9, Getting Your Neurons to Work, the details of graphs and sessions do not appear. However, to compare our brain with deep learning, some details need to be examined using Google_Tensor_Classifier.py, which classifies MNIST handwritten images and saves the summaries (information...