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Large Scale Machine Learning with Python

You're reading from   Large Scale Machine Learning with Python Learn to build powerful machine learning models quickly and deploy large-scale predictive applications

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Product type Paperback
Published in Aug 2016
Publisher Packt
ISBN-13 9781785887215
Length 420 pages
Edition 1st Edition
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Authors (3):
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Alberto Boschetti Alberto Boschetti
Author Profile Icon Alberto Boschetti
Alberto Boschetti
Bastiaan Sjardin Bastiaan Sjardin
Author Profile Icon Bastiaan Sjardin
Bastiaan Sjardin
Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
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Toc

Table of Contents (12) Chapters Close

Preface 1. First Steps to Scalability FREE CHAPTER 2. Scalable Learning in Scikit-learn 3. Fast SVM Implementations 4. Neural Networks and Deep Learning 5. Deep Learning with TensorFlow 6. Classification and Regression Trees at Scale 7. Unsupervised Learning at Scale 8. Distributed Environments – Hadoop and Spark 9. Practical Machine Learning with Spark A. Introduction to GPUs and Theano Index

Chapter 5. Deep Learning with TensorFlow

In this chapter, we will focus on TensorFlow and cover the following topics:

  • Basic TensorFlow operations
  • Machine learning from scratch with TensorFlow—regression, SGD classifier, and neural network
  • Deep learning with SkFlow
  • Incremental deep learning with large files
  • Convolutional Neural Networks with Keras

The TensorFlow framework was introduced at the time of writing this book and already has proven to be a great addition to the machine learning landscape.

TensorFlow was started by the Google Brain Team consisting of most of the researchers that worked on important developments in deep learning in the recent decade (Geoffrey Hinton, Samy Bengio, and others). It is basically a next-generation development of an earlier generation of frameworks called DistBelief, a platform for distributed deep neural networks. Contrary to TensorFlow, DistBelief is not open source. Interesting examples of successful DistBelief projects are the reversed image...

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