Book Image

Machine Learning with TensorFlow 1.x

By : Quan Hua, Saif Ahmed, Shams Ul Azeem
Book Image

Machine Learning with TensorFlow 1.x

By: Quan Hua, Saif Ahmed, Shams Ul Azeem

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.
Table of Contents (13 chapters)
Free Chapter
1
Getting Started with TensorFlow

The Doctor Will See You Now

We have, so far, used deep networks for image, text, and time series processing. While most of our examples were interesting and relevant, they weren't enterprise-grade. Now, we'll tackle an enterprise-grade problem—medical diagnosis. We make the enterprise-grade designation because medical data has attributes one does not typically deal with outside large enterprises, namely proprietary data formats, large native sizes, inconvenient class data, and atypical features.

In this chapter, we will cover the following topics:

  • Medical imaging files and their peculiarities
  • Dealing with large image files
  • Extracting class data from typical medical files
  • Applying networks "pre-trained" with non-medical data
  • Scaling training to accommodate the scale typically with medical data

Obtaining medical data is a challenge on its own, so we...