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

Implementing a multilayer perceptron (MLP)


A perceptron is composed of a single layer of LTUs, with each neuron connected to all the inputs. These connections are often represented using special pass-through neurons called input neurons: they just output whatever input they are fed. Moreover, an extra bias feature is generally added (x0 = 1).

This bias feature is typically represented using a special type of neuron called a bias neuron, which just outputs 1 all the time. A perceptron with two inputs and three outputs is represented in Figure 7. This perceptron can simultaneously classify instances into three different binary classes, which makes it a multioutput classifier:

Figure 7: A perceptron with two inputs and three outputs

Since the decision boundary of each output neuron is linear, perceptrons are incapable of learning complex patterns. However, if the training instances are linearly separable, research has shown that this algorithm will converge to a solution called "perceptron convergence...