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Data Science  with Python

You're reading from   Data Science with Python Combine Python with machine learning principles to discover hidden patterns in raw data

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Product type Paperback
Published in Jul 2019
Publisher Packt
ISBN-13 9781838552862
Length 426 pages
Edition 1st Edition
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Authors (3):
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Rohan Chopra Rohan Chopra
Author Profile Icon Rohan Chopra
Rohan Chopra
Mohamed Noordeen Alaudeen Mohamed Noordeen Alaudeen
Author Profile Icon Mohamed Noordeen Alaudeen
Mohamed Noordeen Alaudeen
Aaron England Aaron England
Author Profile Icon Aaron England
Aaron England
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Toc

Table of Contents (10) Chapters Close

About the Book 1. Introduction to Data Science and Data Pre-Processing FREE CHAPTER 2. Data Visualization 3. Introduction to Machine Learning via Scikit-Learn 4. Dimensionality Reduction and Unsupervised Learning 5. Mastering Structured Data 6. Decoding Images 7. Processing Human Language 8. Tips and Tricks of the Trade 1. Appendix

Convolutional Neural Networks

Convolutional Neural Network (CNN) is the name given to a neural network that has convolutional layers. These convolutional layers handle the high dimensionality of raw images efficiently with the help of convolutional filters. CNNs allow us to recognize highly complex patterns in images, which would be impossible with a simple neural network. CNNs can also be used for natural language processing.

The first few layers of a CNN are convolutional, where the network applies different filters to the image to find useful patterns in the image; then there's the pooling layers, which help down-sample the output of the convolutional layers. The activation layer controls which signal flows from one layer to the next, emulating the neurons in our brain. The last few layers in the network are dense layers; these are the same layers we used for the previous exercise.

Convolutional Layer

The convolutional layer consists of multiple filters that learn to activate when they...

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