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

Overview of Mechanical Turk

Mechanical Turk is a service that allows us to create and manage online human intelligence tasks that will be completed by human workers. There are lots of tasks that humans can do better than computers. Therefore, we can take advantage of this service to support our machine learning system.

You can view this system at https://www.mturk.com. Here is the website of the service:

Here are a couple of examples of tasks that you can use to support your machine learning system:

  • Dataset labeling: You usually have a lot of unlabeled data, and you can use Mechanical Turk to help you build a consistent ground truth for your machine learning workflow.
  • Generate dataset: You can ask the workers to build a large amount of training data. For example, we can ask workers to create text translations or chat sentences for a natural language system. You can ask them...