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Python Data Science Essentials

You're reading from   Python Data Science Essentials A practitioner's guide covering essential data science principles, tools, and techniques

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Product type Paperback
Published in Sep 2018
Publisher Packt
ISBN-13 9781789537864
Length 472 pages
Edition 3rd Edition
Languages
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Authors (2):
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Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
Alberto Boschetti Alberto Boschetti
Author Profile Icon Alberto Boschetti
Alberto Boschetti
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Toc

Table of Contents (11) Chapters Close

Preface 1. First Steps FREE CHAPTER 2. Data Munging 3. The Data Pipeline 4. Machine Learning 5. Visualization, Insights, and Results 6. Social Network Analysis 7. Deep Learning Beyond the Basics 8. Spark for Big Data 9. Strengthen Your Python Foundations 10. Other Books You May Enjoy

Deep Learning Beyond the Basics

In this chapter, we will introduce deep models, and we will show three examples of how to build deep models. More specifically, in this chapter, you'll learn the following:

  • The basics of deep learning
  • How to optimize a deep net
  • The speed/complexity/accuracy problem
  • How to classify images with a CNN
  • How to use a pre-trained network for classification and transfer learning
  • How to operate on sequences using a LSTM

We will be using the Keras package (https://keras.io/), which is a high-level API for deep learning that will render approaching neural networks for deep learning much easier and more understandable because it is characterized by a Lego-like approach (here, the bricks are a neural network's composing elements).

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