Book Image

Deep Learning with TensorFlow - Second Edition

By : Giancarlo Zaccone, Md. Rezaul Karim
Book Image

Deep Learning with TensorFlow - Second Edition

By: Giancarlo Zaccone, Md. Rezaul 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 (15 chapters)
Deep Learning with TensorFlow - Second Edition
Contributors
Preface
Other Books You May Enjoy
Index

CNNs in action


Taking as an example the 5×5 input matrix shown earlier, a CNN is made up of an input layer consisting of 25 neurons (5×5) that has the task of acquiring the input value corresponding to each pixel and transferring it to the next layer.

In a multilayer network, the output from all of the neurons in the input layer would be connected to each neuron in the hidden layer (the fully connected layer). In CNN networks, however, the connection scheme that defines the convolutional layer that we are going to describe is significantly different. As you may be able to guess, this is the main type of layer: the use of one or more of these layers in a CNN is indispensable.

In a convolutional layer, each neuron is connected to a certain region of the input area called the receptive field. For example, using a 3×3 kernel filter, each neuron will have a bias and 9 weights (3×3) connected to a single receptive field. To effectively recognize an image, we need various different kernel filters...