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

Quantum Computing

IBM has begun to build quantum computers for research and business purposes. In a few years, quantum computing will become disruptive and will provide exponential computing power. Google, Xanadu, D-Wave, Rigetti, and others devote research budgets to quantum computing.

This unique computer capacity in the history of humanity opens the doors to obtaining results that would be impossible to obtain with classical computers. In 1994, Peter Shor showed that a quantum algorithm could perform better than a classical one for prime factors of an integer and the discrete logarithm problem. Then in 1995, Lov Grover added the unstructured search problem. With the rise of quantum computers, research can go much further.

Quantum computers in themselves will not provide revolutionary algorithms. Any algorithm can be broken down into components that run basic classical machines. Supercomputers can still run artificial intelligence algorithms much easier...