Chapter 1, Getting Started, introduces different packages that are available for building deep learning models, such as TensorFlow, MXNet, and H2O. and how to set them up to be utilized later in the book.

Chapter 2, Deep Learning with R, introduces the basics of neural network and deep learning. This chapter covers multiple recipes for building a neural network models using multiple toolboxes in R.

Chapter 3, Convolution Neural Network, covers recipes on Convolution Neural Networks (CNN) through applications in image processing and classification.

Chapter 4, Data Representation Using Autoencoders, builds the foundation of autoencoder using multiple recipes and also covers the application in data compression and denoising.

Chapter 5, Generative Models in Deep learning, extends the concept of autoencoders to generative models and covers recipes such as Boltzman machines, restricted Boltzman machines (RBMs), and deep belief networks.

Chapter 6, Recurrent Neural Networks, sets up the foundation for building machine learning models on a sequential datasets using multiple recurrent neural networks (RNNs).

Chapter 7, Reinforcement Leaning, provides the fundamentals for building reinforcement learning using Markov Decision Process (MDP) and covers both model-based learning and model-free learning.

Chapter 8, Application of Deep Learning in Text-Mining, provides an end-to-end implementation of the deep learning text mining domain.

Chapter 9, Application of Deep Learning to Signal processing, covers a detailed case study of deep learning in the signal processing domain.

Chapter 10, Transfer Learning, covers recipes for using pretrained models such as VGG16 and Inception and explains how to deploy a deep learning model using GPU.

A lot of inquisitiveness, perseverance, and passion is required to build a strong background in data science. The scope of deep learning is quite broad; thus, the following backgrounds is required to effectively utilize this cookbook:

Lastly, you need to appreciate deep learning algorithms and know how they solve complex problems in multiple domains.

This book is for data science professionals or analysts who have performed machine learning tasks and now want to explore deep learning and want a quick reference that address the pain points that crop up while implementing deep learning. Those who wish to have an edge over other deep learning professionals will find this book quite useful.

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: "We can include other contexts through the use of the include directive."

A block of code is set as follows:

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

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:

# cp /usr/src/asterisk-addons/configs/cdr_mysql.conf.sample   /etc/asterisk/cdr_mysql.conf

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: "Clicking the Next button moves you to the next screen."

Feedback from our readers is always welcome. Let us know what you think about this book--what you liked or disliked. Reader feedback is important for us as it helps us develop titles that you will really get the most out of.

To send us general feedback, simply email [email protected], and mention the book's title in the subject of your message.

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Downloading the example code

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