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)

GPGPU history

The general purpose computing on graphics processing unit (GPGPU) recognizes the trend to employ GPU technology for non-graphic applications. Until 2006, the graphics API OpenGL and DirectX standards were the only ways to program with the GPU. Any attempt to execute arbitrary calculations on the GPU was subject to the programming restrictions of those APIs.

The GPUs were designed to produce a color for each pixel on the screen using programmable arithmetic units called pixel shaders. The programmers realized that if the inputs were numerical data, with a different meaning from the pixel colors, then they could program the pixel shader to perform arbitrary computations.

The GPU was deceived by showing general tasks such as rendering tasks; this deception was intelligent, but also very convoluted.

There were memory limitations because the programs could only receive a handful of input color and texture...