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Artificial Intelligence By Example

You're reading from   Artificial Intelligence By Example Acquire advanced AI, machine learning, and deep learning design skills

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781839211539
Length 578 pages
Edition 2nd Edition
Languages
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Table of Contents (23) Chapters Close

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 9 – Abstract Image Classification with Convolutional Neural Networks (CNNs)

  1. A convolutional neural network (CNN) can only process images. (Yes | No)

    The answer is no. CNNs can process words, sounds, or video sequences, to classify and predict.

  2. A kernel is a preset matrix used for convolutions. (Yes | No)

    The answer is yes, and no. There are many preset matrices used to process images, such as the one used in Edge_detection_Kernel.py in this chapter. However, in this chapter, kernels were created randomly, and then the network trained their weights to fit the target images.

  3. Does pooling have a pooling matrix, or is it random?

    In some cases, a pooling matrix has a size that is an option when the pooling layer is added to the model, such as a 2×2 pooling window. However, in AutoML neural networks, for example, we can try to run optimizing algorithms that will test various sizes to see which one...

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