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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

Demystifying gradient descent

Before we get into the details, let's understand the basics. What is a function in mathematics? A function represents the relation between input and output. We generally use to denote a function. For instance, implies a function that takes as an input and returns as an output. It can also be represented as .

Here, we have a function, , and we can plot and see what our function looks like:

The smallest value of a function is called the minimum of a function. As you can see in the preceding plot, the minimum of the function lies at 0. The previous function is called a convex function, and is where we have only one minimum value. A function is called a non-convex function when there is more than one minimum value. As we can see in the following diagram, a non-convex function can have many local minima and one global minimum value, whereas...

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