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Machine Learning with TensorFlow 1.x

By : Hua, Ahmed, Ul Azeem

3.8 (8)

Buy this Book

Machine Learning with TensorFlow 1.x

3.8 (8)

By: Hua, Ahmed, Ul Azeem

Buy this Book

Overview of this book

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.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Getting Started with TensorFlow

Current use

Installing TensorFlow

Summary

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

The TensorFlow Toolbox

A quick preview

Installing TensorBoard

Automating runs

Summary

Cats and Dogs

Revisiting notMNIST

Training day

Actual cats and dogs

Saving the model for ongoing use

Using the classifier

Skills learned

Summary

Sequence to Sequence Models-Parlez-vous Français?

A quick preview

Drinking from the firehose

Training day

Summary

Finding Meaning

Additional setup

Skills learned

Summary

Making Money with Machine Learning

Inputs and approaches

Approaching the problem

Taking it further

Practical considerations for the individual

Skills learned

Summary

The Doctor Will See You Now

The challenge

The data

The pipeline

Going further

Skills Learned

Summary

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

Go Live and Go Big

Quick look at Amazon Web Services

Overview of the application

Overview of Mechanical Turk

Summary

Going Further - 21 Problems

Dataset and challenges

TensorFlow-based Projects

Interesting Projects

Caffe to TensorFlow

TensorFlow-Slim

Summary

Advanced Installation

Installation

Using TensorFlow with Anaconda

Summary

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.