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 computing

There are several reasons that have led to deep learning to be developed and placed at the center of attention in the field of machine learning only in recent decades.

One reason, perhaps the main one, is surely represented by the progress in hardware, with the availability of new processors, such as graphics processing units (GPUs), which have greatly reduced the time needed for training networks, lowering them to 10/20 times.

In fact, since the connections between the individual neurons have a weight numerically estimated, and that networks learn by calibrating the weights properly, we understand how the network's complexity requires a huge increase, in computing power, required for graphics processors used in the experiments.