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

Going Further - 21 Problems

In this chapter, we are going to introduce 21 real life problems that you can use deep learning and TensorFlow to tackle. We will start by talking about some public large-scale datasets and competitions. Then, we will show some awesome TensorFlow projects on Github. We will also introduce some interesting projects that have been done in other deep learning frameworks so that you can get inspired and implement your own TensorFlow solution. Finally, we will work through a simple technique to convert a Caffe model to a TensorFlow model and introduce using a high-level TensorFlow library, TensorFlow-Slim.

In this chapter, we will look into the following topics:

  • Large-scale, public datasets and competitions
  • Awesome TensorFlow projects
  • Some inspired deep learning projects from other frameworks
  • Converting a Caffe model to TensorFlow
  • Introducing TensorFlow...