Chapter 1, Maths for Neural Networks, covers the basics of algebra, probability, and optimization techniques for neural networks.

Chapter 2, Deep Feedforward Networks, explains the basics of perceptrons, neurons, and feedforward neural networks. You will also learn about various learning techniques and mainly the core learning algorithm called backpropagation.

Chapter 3, Optimization for Neural Networks, covers optimization techniques that are fundamental to neural network learning.

Chapter 4, Convolutional Neural Networks, discusses the CNN algorithm in detail. CNNs and their application to different data types will also be covered.

Chapter 5, Recurrent Neural Networks, covers the RNN algorithm in detail. RNNs and their application to different data types are covered as well.

Chapter 6, Generative Models, explains the basics of generative models and the different approaches to generative models. 

Chapter 7, Deep Belief Networking, covers the basics of deep belief networks, how they differ from the traditional neural networks, and their implementation.

Chapter 8, Autoencoders, provides an introduction to autoencoders, which have recently come to the forefront of generative modeling.

Chapter 9, Deep Learning Research and Summary, discusses the current and future research details on deep learning. It also points the readers to papers for reference reading.

Appendix, Getting Started with TensorFlow, discusses environment setup of TensorFlow, comparison of TensorFlow with NumPy, and the concept if Auto differentiation 

This book will guide you through the installation of all the tools that you need to follow the examples:

This book is meant for developers with a statistical background who want to work with neural networks. Though we will be using TensorFlow as the underlying library for neural networks, this book can be used as a generic resource to bridge the gap between the math and the implementation of deep learning. If you have some understanding of Tensorflow and Python and want to learn what happens at a level lower than the plain API syntax, this book is for you.

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning. Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The next lines of code read the link and assign it to the BeautifulSoup function." A block of code is set as follows:

#import packages into the project 
from bs4 import BeautifulSoup 
from urllib.request import urlopen 
import pandas as pd

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

 [default] exten 
=> s,1,Dial(Zap/1|30) exten 
=> s,2,Voicemail(u100) exten 
=> s,102,Voicemail(b100) exten 
=> i,1,Voicemail(s0) 

Any command-line input or output is written as follows:

C:\Python34\Scripts> pip install -upgrade pipC:\Python34\Scripts> pip install pandas

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "In order to download new modules, we will go to Files | Settings | Project Name | Project Interpreter."

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