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

Applying artificial intelligence to Amazon's real-time sales, production, and delivery forces projects into reality.

Learning machine learning and deep learning with MNIST, CIFAR, and other ready-to-use datasets with ready-to-use programs is a prerequisite to mastering artificial intelligence. Learning mathematics is a must.

Building a few programs that can do various theoretical things cannot be avoided. However, managing a real project under corporate pressure will bring an AI specialist up to another level. The AI specialist will have to put AI theory into practice. The constraints of corporate specifications make machine learning projects exciting. During those projects, experts learn valuable information on how AI solutions work and can be improved.

This chapter described an RL-DL-CRLMM model with an optimizer. We learned how the market is evolving from planning manufacturing in advance to real-time planning challenging classical processes. We saw...