Machine Learning with TensorFlow

Tackle common commercial machine learning problems with Google’s TensorFlow library and build deployable solutions.

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Machine Learning with TensorFlow

Saif Ahmed

Tackle common commercial machine learning problems with Google’s TensorFlow library and build deployable solutions.

Access cutting-edge content as it's created

Want access to this book right now? Read as we develop it as part of our Early Access program. Click here to find out more about Early Access.

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Book Details

ISBN 139781786462961
Paperback301 pages

Book Description

TensorFlow is an open source software library for numerical computation using data flow graphs. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

This book approaches common commercial machine learning problems using Google’s TensorFlow library. It will cover unique features of the library such as Data Flow Graphs, training, visualisation of performance with TensorBoard—all within an example-rich context using problems from multiple industries. The is on introducing new concepts through problems that are coded and solved over the course of each chapter.

Table of Contents

Chapter 1: Getting Started with TensorFlow
Current use
Installing TensorFlow
Summary
Chapter 2: Your First Classifier
The key parts
Obtaining training data
Downloading training data
Understanding classes
Additional setup
Logical stopping points
The machine learning briefcase
Training day
Saving the model for ongoing use
Why hide the test set?
Using the classifier
Deep diving into the network
Skills learned
Summary
Chapter 3: The TensorFlow Toolbox
A quick preview
Installing TensorBoard
Automating runs
Summary
Chapter 4: Cats and Dogs
Revisiting notMNIST
Training day
Actual cats and dogs
Not reinventing wheels
Saving the model for ongoing use
Using the classifier
Skills learned
Summary
Chapter 5: Sequence to Sequence Models-Parlez-vous Français?
A quick preview
Drinking from the firehose
Training day
Summary
Chapter 6: Finding Meaning
Additional setup
Skills learned
Summary
Chapter 7: Making Money with Machine Learning
Inputs and approaches
Approaching the problem
Visualizing the network
Taking it further
Practical considerations for the individual
Skills learned
Summary

What You Will Learn

  • Set up basic and advanced TensorFlow installations
  • Deep-dive into training, validating, and monitoring training performance
  • Set up and run cross-sectional examples (images, time-series, text, and audio)
  • Create pipelines to deal with real-world input data
  • Set up and run cross domain-specific examples (economics, medicine, text classification, and advertising)
  • Empower the reader to go from concept to a production-ready machine learning setup/pipeline capable of real-world usage

Authors

Table of Contents

Chapter 1: Getting Started with TensorFlow
Current use
Installing TensorFlow
Summary
Chapter 2: Your First Classifier
The key parts
Obtaining training data
Downloading training data
Understanding classes
Additional setup
Logical stopping points
The machine learning briefcase
Training day
Saving the model for ongoing use
Why hide the test set?
Using the classifier
Deep diving into the network
Skills learned
Summary
Chapter 3: The TensorFlow Toolbox
A quick preview
Installing TensorBoard
Automating runs
Summary
Chapter 4: Cats and Dogs
Revisiting notMNIST
Training day
Actual cats and dogs
Not reinventing wheels
Saving the model for ongoing use
Using the classifier
Skills learned
Summary
Chapter 5: Sequence to Sequence Models-Parlez-vous Français?
A quick preview
Drinking from the firehose
Training day
Summary
Chapter 6: Finding Meaning
Additional setup
Skills learned
Summary
Chapter 7: Making Money with Machine Learning
Inputs and approaches
Approaching the problem
Visualizing the network
Taking it further
Practical considerations for the individual
Skills learned
Summary

Book Details

ISBN 139781786462961
Paperback301 pages
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