Machine Learning with TensorFlow 1.x

Tackle common commercial machine learning problems with Google’s TensorFlow 1.x library and build deployable solutions.
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Machine Learning with TensorFlow 1.x

Quan Hua, Shams Ul Azeem, Saif Ahmed

Tackle common commercial machine learning problems with Google’s TensorFlow 1.x library and build deployable solutions.

Quick links: > What will you learn?> Table of content

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Book Details

ISBN 139781786462961
Paperback304 pages

Book Description

Google's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x.

Firstly, you’ll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data flow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You’ll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you’ll implement a complete real-life production system from training to serving a deep learning model. As you advance you’ll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you’ll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim.

By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment.

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
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
Taking it further
Practical considerations for the individual
Skills learned
Summary
Chapter 8: The Doctor Will See You Now
The challenge
The data
The pipeline
Going further
Skills Learned
Summary
Chapter 9: Cruise Control - Automation
An overview of the system
Setting up the project
Loading a pre-trained model to speed up the training
Training the model for our dataset
Serving the model in production
Automatic fine-tune in production
Summary
Chapter 10: Go Live and Go Big
Quick look at Amazon Web Services
Overview of the application
Overview of Mechanical Turk
Summary
Chapter 11: Going Further - 21 Problems
Dataset and challenges
TensorFlow-based Projects
Interesting Projects
Caffe to TensorFlow
TensorFlow-Slim
Summary
Chapter 12: Advanced Installation
Installation
Using TensorFlow with Anaconda
Summary

What You Will Learn

  • Explore how to use different machine learning models to ask different questions of your data
  • Learn how to build deep neural networks using TensorFlow 1.x
  • Cover key tasks such as clustering, sentiment analysis, and regression analysis using TensorFlow 1.x
  • Find out how to write clean and elegant Python code that will optimize the strength of your algorithms
  • Discover how to embed your machine learning model in a web application for increased accessibility
  • Learn how to use multiple GPUs for faster training using AWS

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
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
Taking it further
Practical considerations for the individual
Skills learned
Summary
Chapter 8: The Doctor Will See You Now
The challenge
The data
The pipeline
Going further
Skills Learned
Summary
Chapter 9: Cruise Control - Automation
An overview of the system
Setting up the project
Loading a pre-trained model to speed up the training
Training the model for our dataset
Serving the model in production
Automatic fine-tune in production
Summary
Chapter 10: Go Live and Go Big
Quick look at Amazon Web Services
Overview of the application
Overview of Mechanical Turk
Summary
Chapter 11: Going Further - 21 Problems
Dataset and challenges
TensorFlow-based Projects
Interesting Projects
Caffe to TensorFlow
TensorFlow-Slim
Summary
Chapter 12: Advanced Installation
Installation
Using TensorFlow with Anaconda
Summary

Book Details

ISBN 139781786462961
Paperback304 pages
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