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Mastering Predictive Analytics with Python

You're reading from Mastering Predictive Analytics with Python

Product type Book

Published in Aug 2016

Publisher

ISBN-13 9781785882715

Pages 334 pages

Edition 1st Edition

Languages

Concepts

Predictive Analytics

Author (1):

Joseph Babcock

Table of Contents (16) Chapters

Mastering Predictive Analytics with Python

Credits

About the Author

About the Reviewer

www.PacktPub.com

Preface

1. From Data to Decisions – Getting Started with Analytic Applications

2. Exploratory Data Analysis and Visualization in Python

3. Finding Patterns in the Noise – Clustering and Unsupervised Learning

4. Connecting the Dots with Models – Regression Methods

5. Putting Data in its Place – Classification Methods and Analysis

6. Words and Pixels – Working with Unstructured Data

7. Learning from the Bottom Up – Deep Networks and Unsupervised Features

8. Sharing Models with Prediction Services

9. Reporting and Testing – Iterating on Analytic Systems

Index

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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Authors (1)

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.

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