Overview of this book

If you're aware of the buzz surrounding the terms such as "machine learning," "artificial intelligence," or "deep learning," you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that. You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders. By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow constructs.
Title Page
Credits
www.PacktPub.com
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Preface
Free Chapter
Maths for Neural Networks
Deep Feedforward Networks
Optimization for Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Generative Models
Autoencoders
Research in Neural Networks
Getting started with TensorFlow

Defining feedforward networks

Deep feedforward networks, also called feedforward neural networks, are sometimes also referred to as Multilayer Perceptrons (MLPs). The goal of a feedforward network is to approximate the function of f∗. For example, for a classiﬁer, y=f∗(x) maps an input x to a label y. A feedforward network defines a mapping from input to label y=f(x;θ). It learns the value of the parameter θ that results in the best function approximation.

We discuss RNNs in Chapter 5Recurrent Neural Networks. Feedforward networks are a conceptual stepping stone on the path to recurrent networks, which power many natural language applications. Feedforward neural networks are called networks because they compose together many diﬀerent functions which represent them. These functions are composed in a directed acyclic graph.

The model is associated with a directed acyclic graph describing how the functions are composed together. For example, there are three functions f(1), f(2), and f(3) connected...