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

Feed-forward neural networks (FFNNs)


An FFNN consists of a large number of neurons, organized in layers: one input layer, one or more hidden layers, and one output layer. Each neuron in a layer is connected to all the neurons of the previous layer, although the connections are not all the same because they have different weights. The weights of these connections encode the knowledge of the network. Data enters at the inputs and passes through the network, layer by layer until it arrives at the outputs. During this operation, there is no feedback between layers. Therefore, these types of networks are called feed-forward neural networks.

An FFNN with enough neurons in the hidden layer is able to approximate with arbitrary precision, and can model the linear, as well as non-linear, relationships in your data:

  • Any continuous function, with one hidden layer

  • Any function, even discontinuous, with two hidden layers

However, it is not possible to determine a priori, with adequate precision, the required...