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You're reading from  Mastering Predictive Analytics with Python

Product typeBook
Published inAug 2016
Reading LevelIntermediate
Publisher
ISBN-139781785882715
Edition1st Edition
Languages
Python
Concepts
Predictive Analytics
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Author (1)
Joseph Babcock
Joseph Babcock
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Joseph Babcock

Joseph Babcock has spent more than a decade working with big data and AI in the e-commerce, digital streaming, and quantitative finance domains. Through his career he has worked on recommender systems, petabyte scale cloud data pipelines, A/B testing, causal inference, and time series analysis. He completed his PhD studies at Johns Hopkins University, applying machine learning to the field of drug discovery and genomics.
Read more about Joseph Babcock

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The TensorFlow library and digit recognition

For the exercises in this chapter, we will be using the TensorFlow library open-sourced by Google (available at https://www.tensorflow.org/). Installation instructions vary by operating system. Additionally, for Linux systems, it is possible to leverage both the CPU and graphics processing unit (GPU) on your computer to run deep learning models. Because many of the steps in training (such as the multiplications required to update a grid of weight values) involve matrix operations, they can be readily parallelized (and thus accelerated) by using a GPU. However, the TensorFlow library will work on CPU as well, so don't worry if you don't have access to an Nvidia GPU card.

The MNIST data

The data we will be examining in this exercise is a set of images of hand-drawn numbers from 0 to 9 from the Mixed National Institute of Standards and Technology (MNIST) database (LeCun, Yann, Corinna Cortes, and Christopher JC Burges. The MNIST database of handwritten...

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Mastering Predictive Analytics with Python
Published in: Aug 2016Publisher: ISBN-13: 9781785882715
© 2016 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Joseph Babcock

Joseph Babcock has spent more than a decade working with big data and AI in the e-commerce, digital streaming, and quantitative finance domains. Through his career he has worked on recommender systems, petabyte scale cloud data pipelines, A/B testing, causal inference, and time series analysis. He completed his PhD studies at Johns Hopkins University, applying machine learning to the field of drug discovery and genomics.
Read more about Joseph Babcock

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