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Hands-On Mathematics for Deep Learning

You're reading from  Hands-On Mathematics for Deep Learning

Product type Book
Published in Jun 2020
Publisher Packt
ISBN-13 9781838647292
Pages 364 pages
Edition 1st Edition
Languages
Author (1):
Jay Dawani Jay Dawani
Profile icon Jay Dawani

Table of Contents (19) Chapters

Preface 1. Section 1: Essential Mathematics for Deep Learning
2. Linear Algebra 3. Vector Calculus 4. Probability and Statistics 5. Optimization 6. Graph Theory 7. Section 2: Essential Neural Networks
8. Linear Neural Networks 9. Feedforward Neural Networks 10. Regularization 11. Convolutional Neural Networks 12. Recurrent Neural Networks 13. Section 3: Advanced Deep Learning Concepts Simplified
14. Attention Mechanisms 15. Generative Models 16. Transfer and Meta Learning 17. Geometric Deep Learning 18. Other Books You May Enjoy

Dataset augmentation

Deep feedforward networks, as we have learned, are very data-hungry and they use all this data to learn the underlying data distribution so that they can use their gained knowledge to make predictions on unseen data. This is because the more data they see, the more likely it is that what they encounter in the test set will be an interpolation of the distribution they have already learned. But getting a large enough dataset with good-quality labeled data is by no means a simple task (especially for certain problems where gathering data could end up being very costly). A method to circumvent this issue is using data augmentation; that is, generating synthetic data and using it to train our deep neural network.

The way synthetic data generation works is that we use a generative model (more on this in Chapter 12, Generative Models) to learn the underlying distribution...

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