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

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

Chapter 5 – How to Use Decision Trees to Enhance K-Means Clustering

The questions will focus on the hyperparameters.

The number of k clusters is not that important. (Yes | No)
The answer is no. The number of clusters requires careful selection, possibly a trial-and-error approach. Each project will lead to different clusters.
Mini-batches and batches contain the same amount of data. (Yes | No)
The answer is no. "Batch" generally refers to the dataset, and "mini-batch" represents a "subset" of data.
K-means can run without mini-batches. (Yes | No)
The answer is yes, and no. If the volume of data remains small, then the training epochs can run on the whole dataset. If the data volume exceeds a reasonable amount of computer power (CPU or GPU), mini-batches must be created to optimize training computation.
Must centroids be optimized for result acceptance? (Yes | No) ...