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 pipeline

We will go about this intelligently. There are a lot of pipeline model structures made by Google using different networks in their TensorFlow library. What we'll do here is take one of those model structures and networks and modify the code to our needs.

This is good because we won't waste our time building a pipeline from scratch and won't have to worry about incorporating the TensorBoard visualization stuff as it is already present in the Google pipeline models.

We will use a pipeline model from here:

https://github.com/tensorflow/models/

As you can see, there are a lot of different models made in TensorFlow in this repository. You can dive deeper into some models that are related to natural language processing (NLP), recursive neural networks, and other topics. This is a really good place to start if you want to understand complex models.

For this...