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Hands-On Deep Learning Algorithms with Python

You're reading from   Hands-On Deep Learning Algorithms with Python Master deep learning algorithms with extensive math by implementing them using TensorFlow

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Product type Paperback
Published in Jul 2019
Publisher Packt
ISBN-13 9781789344158
Length 512 pages
Edition 1st Edition
Languages
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Author (1):
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Sudharsan Ravichandiran Sudharsan Ravichandiran
Author Profile Icon Sudharsan Ravichandiran
Sudharsan Ravichandiran
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Getting Started with Deep Learning
2. Introduction to Deep Learning FREE CHAPTER 3. Getting to Know TensorFlow 4. Section 2: Fundamental Deep Learning Algorithms
5. Gradient Descent and Its Variants 6. Generating Song Lyrics Using RNN 7. Improvements to the RNN 8. Demystifying Convolutional Networks 9. Learning Text Representations 10. Section 3: Advanced Deep Learning Algorithms
11. Generating Images Using GANs 12. Learning More about GANs 13. Reconstructing Inputs Using Autoencoders 14. Exploring Few-Shot Learning Algorithms 15. Assessments 16. Other Books You May Enjoy

Momentum-based gradient descent

In this section, we will learn about two new variants of gradient descent, called momentum and Nesterov accelerated gradient.

Gradient descent with momentum

We have a problem with SGD and mini-batch gradient descent due to the oscillations in the parameter update. Take a look at the following plot, which shows how mini-batch gradient descent is attaining convergence. As you can see, there are oscillations in the gradient steps. The oscillations are shown by the dotted line. As you may notice, it is making a gradient step toward one direction, and then taking a different direction, and so on, until it reaches convergence:

This oscillation occurs because, since we update the parameters after...

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