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Deep Learning for Beginners

You're reading from   Deep Learning for Beginners A beginner's guide to getting up and running with deep learning from scratch using Python

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Product type Paperback
Published in Sep 2020
Publisher Packt
ISBN-13 9781838640859
Length 432 pages
Edition 1st Edition
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Authors (2):
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Pablo Rivas Pablo Rivas
Author Profile Icon Pablo Rivas
Pablo Rivas
Dr. Pablo Rivas Dr. Pablo Rivas
Author Profile Icon Dr. Pablo Rivas
Dr. Pablo Rivas
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Toc

Table of Contents (20) Chapters Close

Preface 1. Section 1: Getting Up to Speed
2. Introduction to Machine Learning FREE CHAPTER 3. Setup and Introduction to Deep Learning Frameworks 4. Preparing Data 5. Learning from Data 6. Training a Single Neuron 7. Training Multiple Layers of Neurons 8. Section 2: Unsupervised Deep Learning
9. Autoencoders 10. Deep Autoencoders 11. Variational Autoencoders 12. Restricted Boltzmann Machines 13. Section 3: Supervised Deep Learning
14. Deep and Wide Neural Networks 15. Convolutional Neural Networks 16. Recurrent Neural Networks 17. Generative Adversarial Networks 18. Final Remarks on the Future of Deep Learning 19. Other Books You May Enjoy

Questions and answers

  1. What data summarization strategy discussed in this chapter can reduce the dimensionality of a convolutional model?

Pooling.

  1. Does adding more convolutional layers make the network better?

Not always. It has been shown that more layers has a positive effect on networks, but there are certain occasions when there is no gain. You should determine the number of layers, filter sizes, and pooling experimentally.

  1. What other applications are there for CNNs?

Audio processing and classification; image denoising; image super-resolution; text summarization and other text-processing and classification tasks; the encryption of data.

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