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

Taking it further

Suppose you just trained a nifty classifier showing some predictive power over the markets, should you start trading? Much like with the other machine learning projects we've done to date, you will need to test on an independent test set. In the past, we've often cordoned off our data into the following three sets:

  • The training set
  • The development set, aka the validation set
  • The test set

We can do something similar to our current work, but the financial markets give us an added resource—ongoing data streams!

We can use the same data source we used for our earlier pulls and continue to pull more data; essentially, we have an ever-expanding, unseen dataset! Of course, some of this depends on the frequency of the data that we use—if we operate on daily data, it will take a while to accomplish this. Operating on hourly or per-minute data...