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)

Introducing CNNs

In recent years, Deep Neural Networks (DNNs) have contributed a new impetus to research as well as industry and are therefore been used increasingly. A special type of a DNN is a Convolutional Neural Network (CNN), which has been used with great success in image classification problems.

Before diving into the implementation of an image classifier based on CNN, we'll introduce some basic concepts in image recognition, such as feature detection and convolution.

It's well known that a real image is associated with a grid composed of a high number of small squares, called pixels. The following figure represents a black and white image related to a 5x5 grid of pixels:

Black and white image

Each element of the grid corresponds to a pixel and, in the case of a black and white image, it assumes either a value of 1, which is associated with black color or the value 0, which is associated with...