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Machine Learning with TensorFlow 1.x
Machine Learning with TensorFlow 1.x

Machine Learning with TensorFlow 1.x: Second generation machine learning with Google's brainchild - TensorFlow 1.x

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Machine Learning with TensorFlow 1.x

Your First Classifier

With TensorFlow now installed, we need to kick the tires. We will do so by writing our first classifier and then training and testing it from start to finish!

Our first classifier will be a handwriting recognizer. One of the most common datasets to train is the MNIST handwritten digits dataset. We'll be using a similar dataset called notMNIST, which features the first ten letters of the English alphabet.

The key parts

There are three key parts to most machine learning classifiers, which are as follows:

  • The training pipeline
  • The neural network setup and training outputs
  • The usage pipeline

The training pipeline obtains data, stages it, cleanses it, homogenizes it, and puts it in a format acceptable to the neural network. Do not be surprised if the training pipeline takes 80% to 85% of your effort initially—this is the reality of most machine learning work. Generally, the more realistic the training data, the more time spent on the training pipeline. In enterprise settings, the training pipeline might be an ongoing effort being enhanced perpetually. This is especially true as datasets get larger.

The second part, the neural network setup, and training, can be quick for routine problems and can be a research-grade effort for harder problems. You may find yourself making small...

Obtaining training data

Machine learning requires training data—often a lot of training data. One of the great things about machine learning is the availability of standard training datasets. These are often used to benchmark node models and configurations and provide a consistent yardstick to gauge performance against previous progress. Many of the datasets are also used in annual global competitions.

This chapter uses training data, which is kindly provided by Yaroslav Bulatov, a machine learning researcher.

Downloading training data

You should start by downloading the training data from the following links:

We will download this programmatically, but we should start with a manual download just to peek at the data and structure of the archive. This will be important when we write the pipeline, as we'll need to understand the structure so we can manipulate the data.

The small set is ideal for peeking. You can do this via the following command line, or just use a browser to download the file with an unarchiver to extract the files (I suggest getting familiarized with the command line as all of this needs to be automated):

cd ~/workdir
wget http://yaroslavvb.com/upload/notMNIST/notMNIST_small.tar.gz
tar xvf notMNIST_small.tar.gz

The preceding command line will...

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Key benefits

  • Enter the new era of second-generation machine learning with Python with this practical and insightful guide
  • Set up TensorFlow 1.x for actual industrial use, including high-performance setup aspects such as multi-GPU support
  • Create pipelines for training and using applying classifiers using raw real-world data

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 ?ow 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.

Who is this book for?

This book is for data scientists and researchers who are looking to either migrate from an existing machine learning library or jump into a machine learning platform headfirst. The book is also for software developers who wish to learn deep learning by example. Particular focus is placed on solving commercial deep learning problems from several industries using TensorFlow’s unique features. No commercial domain knowledge is required, but familiarity with Python and matrix math is expected.

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
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Publication date : Nov 21, 2017
Length: 304 pages
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Language : English
ISBN-13 : 9781786462961
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Table of Contents

12 Chapters
Getting Started with TensorFlow Chevron down icon Chevron up icon
Your First Classifier Chevron down icon Chevron up icon
The TensorFlow Toolbox Chevron down icon Chevron up icon
Cats and Dogs Chevron down icon Chevron up icon
Sequence to Sequence Models-Parlez-vous Français? Chevron down icon Chevron up icon
Finding Meaning Chevron down icon Chevron up icon
Making Money with Machine Learning Chevron down icon Chevron up icon
The Doctor Will See You Now Chevron down icon Chevron up icon
Cruise Control - Automation Chevron down icon Chevron up icon
Go Live and Go Big Chevron down icon Chevron up icon
Going Further - 21 Problems Chevron down icon Chevron up icon
Advanced Installation Chevron down icon Chevron up icon

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Igidok Dec 14, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This Book is very detailed and walks you through the Tensorflow library very well. I am fairly new to Machine Learning and I think this is a good step by guide.
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Amazon Customer Feb 28, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A very useful resource!
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K. Witek Feb 28, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Always interested in what technologies Google is using to run its systems and this is one of them. Machine Learning and AI are the next big thing and this is one of the enabling technologies that's "hot" right now; and for the right reasons. This book is a good way to learn and get established with this technology, although you do have to be fairly technical to make the most out of it. What is particularly interesting for me is how Machine Learning bridges the gap between how computer think and how people think - Machine learning is comprised of algorithms that teach computers to perform tasks that human beings do naturally on a daily basis. This book explores this very aspect and it is very eye opening.
Amazon Verified review Amazon
SRA Feb 28, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I’m brand new to using TensorFlow so this was a good Primer. It also features advanced chapters to take things forward if you’re already familiar with the basics. There were some simplistic examples which were perfect for a starter. All in all, I would recommend this book to new learners and advanced learners as both can benefit.
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Madeeha Husain Feb 28, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is great and has helped me immensely. I’m quite new to TensorFlow and this book has been so much help!
Amazon Verified review Amazon
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