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

Approaching the problem

In this chapter, we will find out whether the stock prices will rise or fall depending on the rises and falls of markets in other time zones (such that their closing time is earlier than the stock in which we want to invest in). We will analyze the data from European markets that close about 3 or 4 hours before the American stock markets. From Quandl, we will get the data from the following European markets:

  • WSE/OPONEO_PL
  • WSE/VINDEXUS
  • WSE/WAWEL
  • WSE/WIELTON

And we will predict the closing rise and fall for the following American market: WIKI/SNPS.

We will download all the market data, view the downloaded graphs for the markets' closing values, and modify the data so that it can be trained on our networks. Then, we'll see how our networks perform on our assumptions.

The code and analysis techniques used in this chapter are inspired by Google&apos...