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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 FREE CHAPTER
2. Introduction to Deep Learning 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

CNN architectures

In this section, we will explore different interesting types of CNN architecture. When we say different types of CNN architecture, we basically mean how convolutional and pooling layers are stacked on each other. Additionally, we will learn how many numbers of convolutional, pooling, and fully connected layers are used, what the number of filters and filter sizes are, and more.

LeNet architecture

The LeNet architecture is one of the classic architectures of a CNN. As shown in the following diagram, the architecture is very simple, and it consists of only seven layers. Out of these seven layers, there are three convolutional layers, two pooling layers, one fully connected layer, and one output layer. It uses...

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