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

Optimizing Your Solutions with K-Means Clustering

No matter how much we know, the key point is the ability to deliver an artificial intelligence (AI) solution. Implementing a machine learning (ML) or deep learning (DL) program remains difficult and will become more complex as technology progresses at exponential rates.

There is no such thing as a simple or easy way to design AI systems. A system is either efficient or not, beyond being either easy or not. Either the designed AI solution provides real-life practical uses, or it builds up into a program that fails to work in various environments beyond the scope of its training sets.

This chapter doesn't deal with how to build the most difficult system possible to show off our knowledge and experience. It faces the hard truth of real-life delivery and ways to overcome obstacles. For example, without the right datasets, your project will never take off. Even an unsupervised ML program requires reliable data in...