Book Image

Deep Learning with TensorFlow

By : Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy
Book Image

Deep Learning with TensorFlow

By: Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy

Overview of this book

Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you’ll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.
Table of Contents (11 chapters)

GPU Computing

Deep Neural Networks (DNNs) are structured in a very uniform manner, such that, at each layer of a network thousands of identical artificial neurons perform the same computation. Therefore, DNN's architecture fits quite well with the kinds of computation that a GPU can efficiently perform.

GPU have additional advantages over CPU; these include having more computational units and having a higher bandwidth to retrieve from memory.

Furthermore, in many deep learning applications that require a lot of computational effort, GPU graphics specific capabilities can be exploited to further speed up calculations.

This chapter is organized as follows:

  • GPGPU computing
  • GPGPU history
  • The CUDA architecture
  • GPU programming model
  • TensorFlow GPU set up
  • TensorFlow GPU management
  • Assigning a single GPU on a multi-GPU system
  • Using multiple GPUs
...