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

Building the RL-DL-CRLMM

The full code of the RL-DL-CRLMM program is RL_DL.py. It is built on the knowledge and programs of the previous chapters and previous sections of this chapter.

The RL-DL-CRLMM contains three components:

A CRLMM convolutional network that will analyze each frame it receives from the webcam that is located right over the pieces of garment packs on the conveyor belt coming from the cutting section.
An optimizer using a modified version of the Z(X) described previously that plans how the assembly stations will be loaded in real-time.
An MDP that will receive the input of the optimizer function and schedule the work of the assembly stations. It also produces the modified Z(X) updated value of the weights of each assembly station for the next frame.

In the physical world, the conveyor belt transports the garment packs, a picture (frame) is taken every n seconds, and the RL-DL-CRLMM runs. The output of the RL-DL-CRLMM sends...