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

Additional setup

Additional setup is required to include libraries required for text processing. Take a look at the following points:

  1. First is Bazel. On Ubuntu, you will need to follow the official tutorial on this link to install Bazel. https://docs.bazel.build/versions/master/install-ubuntu.html. On macOS, you can use HomeBrew to install bazel as follows:
      $ brew install bazel
  1. Then, we will install swig, which will allow us to wrap C/C++ functions to allow calls in Python. On Ubuntu, you can install it using:
      $ sudo apt-get install swig

On Mac OS, we will also install it using brew, as follows:

      $ brew install swig
  1. Next, we'll install the protocol buffer support, which will allow us to store and retrieve serialized data in a more efficient manner than with XML. We specifically need version 3.3.0 to install it as follows:
      $ pip install -U protobuf...