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

AI as a new frontier

Google has a great, but limited, translation program. Use the flaws to innovate! AI research and development has just scratched the surface of the innovations to come.

First, implement an AI solution. Then, use it for what it is. But don't accept its limits. Don't be negative about it. Innovate! Imagine ideas or listen to other ideas you like and build solutions in a team! Google might even publish your solutions!

Improving Google Translate for any translation is impossible. A realistic approach is to focus on customizing Google Translate for a given domain, such as the transportation company in this example. In the next section, we will focus on ways to customize Google Translate.

Lexical field and polysemy

Google_Translate_Customized.py will provide ideas on how to improve Google Translate in a specific area. This section focuses on the transportation vocabulary error Google Translate made. Once again, Google may rapidly correct...