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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

Summary

In this chapter, we saw the essentials and some advanced models for deep networks. We were introduced to how neural networks work and the difference between shallow networks and deep learning. Then, we learnt ho to build a CNN deep network capable of classifying images of traffic signs. We also predicted the class of an image using a pre-trained network. Detecting the sentiment of a movie review using text found in reviews was also a part of the learning.

Deep learning models are indeed very powerful, though at the cost of having many degrees of freedom to handle and many coefficients to train, which requires having at hand large amounts of data.

In the next chapter, we'll see how Spark helps when the amount of data becomes too large to be handled and processed by a single computer.

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