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

Finding Meaning

So far, we mostly used TensorFlow for image processing, and to a lesser extent, for text-sequence processing. In this chapter, we will revisit the written word to find meaning in text. This is part of an area that is commonly termed Natural Language Processing (NLP). Some of the activities in this area include the following:

  • Sentiment analysis—This extracts a general sentiment category from text without extracting the subject or action of the sentence
  • Entity extraction—This extracts the subject, for example, person, place, and event, from a piece of text
  • Keyword extraction—This extracts key terms from a piece of text
  • Word-relation extraction—This extracts not only entities but also the associated action and parts of speech of each

This is just scratching the surface of NLP—there are other techniques, as well as a range of sophistication...