Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Deep Learning with TensorFlow
  • Toc
  • feedback
Deep Learning with TensorFlow

Deep Learning with TensorFlow

By : Zaccone, Karim
3 (4)
close
Deep Learning with TensorFlow

Deep Learning with TensorFlow

3 (4)
By: Zaccone, Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
Table of Contents (13 chapters)
close
12
Index

Main concepts of CNNs


Recently, Deep Neural Networks (DNNs) have given fresh impetus to research and therefore they are being used widely. CNNs are a special type of DNN, and they have been used with great success in image classification problems. Before diving into the implementation of an image classifier based on CNNs, we'll introduce some basic concepts in image recognition, such as feature detection and convolution.

In computer vision, it is 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 5×5 grid of pixels:

Figure 1: Pixel view of a black and white image.

Each element in the grid corresponds to a pixel. In the case of a black and white image, a value of 1 is associated with black and a value of 0 is associated with white. Alternatively, for a grayscale image, the allowed values for each grid element are in the range [0, 255], where 0 is associated with...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete