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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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Toc

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

Getting to Know TensorFlow

In this chapter, we will learn about TensorFlow, which is one of the most popularly used deep learning libraries. Throughout this book, we will be using TensorFlow to build deep learning models from scratch. So, in this chapter, we will get the hang of TensorFlow and its functionalities. We will also learn about TensorBoard, which is a visualization tool provided by TensorFlow used for visualizing models. Moving on, we will learn how to build our first neural network, using TensorFlow to perform handwritten digit classification. Following that, we will learn about TensorFlow 2.0, which is the latest version of TensorFlow. We will learn how TensorFlow 2.0 differs from its previous versions and how it uses Keras as its high-level API.

In this chapter, we will cover the following topics:

  • TensorFlow
  • Computational graphs and sessions
  • Variables, constants...
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