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

Inputs and approaches

Investment firms' internal proprietary trading groups use a large variety of means to invest, trade, and make money. Hedge funds, which are relatively unregulated, use an even broader, more interesting, and more sophisticated means for investment. Some investments are gut-driven or driven by a great deal of thinking. Others are largely filter-driven, algorithmic, or signal-driven. Both approaches are fine, but we'll of course focus on the latter category.

Amongst the quantitative approaches, there are numerous techniques; some of them are as follows:

  • Valuation based
  • Anomaly and signal based
  • External signal based
  • Filtering and segmentation-based cohort analysis

Some of these approaches will use traditional machine learning techniques, such as K-Nearest Neighbors, Naive Bayes, and Support Vector Machines. Cohort analysis, in particular, is almost...