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)

The CUDA architecture

In 2006, NVIDIA was presented as the first GPU to support DirectX 10; the GeForce 8800GTX was also the first GPU to use the CUDA architecture. This architecture included several new components designed specifically for GPU computing and aimed to remove the limitations that prevented them that previous GPUs were used for non-graphical calculations. In fact, the execution units on the GPU could read and write arbitrary memory as well as access a cache maintained in software called shared memory. These architectural features were added to make a CUDA GPU that also excelled in general purpose calculations as well as in traditional graphics tasks.

The following figure summarizes the division of space between the various components of a graphics processing unit (GPU) and a central processing unit (CPU). As you can see, a GPU devotes more transistors to data processing; it is a highly parallel, multithreaded...