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

The original perceptron could not solve the XOR function

The original perceptron was designed in the 1950s and improved in the late 1970s. The original perceptron contained one neuron that could not solve the XOR function.

An XOR function means that you have to choose an exclusive OR (XOR).

This can be difficult to grasp, as we're not used to thinking about the way in which we use or in our everyday lives. In truth, we use or interchangeably as either inclusive or exclusive all of the time. Take this simple example:

If a friend were to come and visit me, I may ask them, "Would you like tea or coffee?" This is basically the offer of tea XOR coffee; I would not expect my friend to ask for both tea and coffee! My friend will choose one or the other.

I may follow up my question with, "Would you like milk or sugar?" In this case, I would not be surprised if my friend wanted both. This is an inclusive or.

XOR, therefore, means "You...