![to the ancient Hindu epics, there are numerous such examples,
which clearly suggest people's interest and inclination towards
creating and having an artificial life.
During the initial computer generations, people had always
wondered if the computer could ever become as intelligent as a
human being! Going forward, even in medical science, the need of
automated machines has become indispensable and almost
unavoidable. With this need and constant research in the same field,
Artificial Intelligence (AI) has turned out to be a flourishing
technology with various applications in several domains, such as
image processing, video processing, and many other diagnosis tools
in medical science too.
Although there are many problems that are resolved by AI systems
on a daily basis, nobody knows the specific rules for how an AI
system is programmed! A few of the intuitive problems are as
follows:
Google search, which does a really good job of understanding
what you type or speak
As mentioned earlier, Facebook is also somewhat good at
recognizing your face, and hence, understanding your interests
Moreover, with the integration of various other fields, for example,
probability, linear algebra, statistics, machine learning, deep
learning, and so on, AI has already gained a huge amount of
popularity in the research field over the course of time.
One of the key reasons for the early success of AI could be that it
basically dealt with fundamental problems for which the computer
did not require a vast amount of knowledge. For example, in 1997,
IBM's Deep Blue chess-playing system was able to defeat the world
champion Garry Kasparov [1]. Although this kind of achievement at
that time can be considered significant, it was definitely not a
burdensome task to train the computer with only the limited number
of rules involved in chess! Training a system with a fixed and limited
number of rules is termed as hard-coded knowledge of the
computer. Many Artificial Intelligence projects have undergone this
hard-coded knowledge about the various aspects of the world in
many traditional languages. As time progresses, this hard-coded](https://image.slidesharecdn.com/102603-250517024311-fac47e49/85/Deep-Learning-with-Hadoop-1st-Edition-Dipayan-Dev-32-320.jpg)
![factors, a subset that might be applicable for each particular sub-
task. A simple illustration is given in the following Figure 1.2:
Figure 1.2: The figure illustrates representation learning. The middle
layers are able to discover the explanatory factors (hidden layers, in
blue rectangular boxes). Some of the factors explain each task's
target, whereas some explain the inputs
However, dealing with extracting some high-level data and features
from a massive amount of raw data, which requires some sort of
human-level understanding, has shown its limitations. There can be
many such examples:
Differentiating the cry of two similar age babies.
Identifying the image of a cat's eye at both day and night time.
This becomes clumsy, because a cat's eyes glow at night unlike
during the daytime.
In all these preceding edge cases, representation learning does not
appear to behave exceptionally, and shows deterrent behavior.
Deep learning, a sub-field of machine learning, can rectify this
major problem of representation learning by building multiple levels
of representations or learning a hierarchy of features from a series
of other simple representations and features [2] [8].](https://image.slidesharecdn.com/102603-250517024311-fac47e49/85/Deep-Learning-with-Hadoop-1st-Edition-Dipayan-Dev-36-320.jpg)
![incrementally extract the abstract features of the data. As these
values are not available or visible in the given data, these layers are
termed as hidden layers.
In the image, from the first layer of data, the edges can easily be
identified just by a comparative study of the neighboring pixels. The
second hidden layer can distinguish the corners and contours from
the first hidden layer's description of the edges. From this second
hidden layer, which describes the corners and contours, the third
hidden layer can identify the different parts of the specific objects.
Ultimately, the different objects present in the image can be
distinctly detected from the third layer.
Deep learning started its journey exclusively in 2006, Hinton et al.
in 2006[2]; also Bengio et al. in 2007[3] initially focused on the
MNIST digit classification problem. In the last few years, deep
learning has seen major transitions from digits to object recognition
in natural images. Apart from this, one of the major breakthroughs
was achieved by Krizhevsky et al. in 2012 [4] using the ImageNet
dataset.
The scope of this book is mainly limited to deep learning, so before
diving into it directly, the necessary definitions of deep learning
should be discussed.
Many researchers have defined deep learning in many ways, and
hence, in the last 10 years, it has gone through many definitions
too! The following are few of the widely accepted definitions:
As noted by GitHub, deep learning is a new area of machine
learning research, which has been introduced with the objective
of moving machine learning closer to one of its original goals:
Artificial Intelligence. Deep learning is about learning multiple
levels of representation and abstraction, which help to make
sense of data such as images, sounds, and texts.
As recently updated by Wikipedia, deep learning is a branch of
machine learning based on a set of algorithms that attempt to
model high-level abstractions in the data by using a deep graph
with multiple processing layers, composed of multiple linear and
non-linear transformations.](https://image.slidesharecdn.com/102603-250517024311-fac47e49/85/Deep-Learning-with-Hadoop-1st-Edition-Dipayan-Dev-38-320.jpg)
![As the definitions suggest, deep learning can also be considered as a
special type of machine learning. Deep learning has achieved
immense popularity in the field of data science with its ability to
learn complex representation from various simple features. To have
an in-depth grip on deep learning, we have listed out a few
terminologies which will be frequently used in the upcoming
chapters. The next topic of this chapter will help you to lay a
foundation for deep learning by providing various terminologies and
important networks used for deep learning.
Getting started with deep
learning
To understand the journey of deep learning in this book, one must
know all the terminologies and basic concepts of machine learning.
However, if you already have enough insight into machine learning
and related terms, you should feel free to ignore this section and
jump to the next topic of this chapter. Readers who are enthusiastic
about data science, and want to learn machine learning thoroughly,
can follow Machine Learning by Tom M. Mitchell (1997) [5] and
Machine Learning: a Probabilistic Perspective (2012) [6].
Note
Neural networks do not perform miracles. But, used sensibly,
they can produce some amazing results.
Deep feed-forward networks
Neural networks can be recurrent as well as feed-forward. Feed-
forward networks do not have any loop associated in their graph,
and are arranged in a set of layers. A network with many layers is
said to be a deep network. In simple words, any neural network with
two or more layers (hidden) is defined as a deep feed-forward
network or feed-forward neural network. Figure 1.4 shows a
generic representation of a deep feed-forward neural network.
Deep feed-forward networks work on the principle that with an
increase in depth, the network can also execute more sequential](https://image.slidesharecdn.com/102603-250517024311-fac47e49/85/Deep-Learning-with-Hadoop-1st-Edition-Dipayan-Dev-39-320.jpg)
![Figure 1.8: Figure shows the impact of a large amount of unlabelled
data during the semi-supervised learning technique. Figure obtained
from Wikipedia
In the preceding Figure 1.8, at the top it shows the decision
boundary that the model uses after distinguishing the white and
black circles. The figure at the bottom displays another decision
boundary, which the model embraces. In that dataset, in addition to
two different categories of circles, a collection of unlabeled data
(grey circle) is also annexed. This type of training can be viewed as
creating the cluster, and then marking those with the labelled data,
which moves the decision boundary away from the high-density data
region.
The preceding Figure 1.8 depicts the illustration of semi-supervised
learning. You can refer to Chapelle et al.'s book [7] to know more
about semi-supervised learning methods.
So, as you have already got a foundation in what Artificial
Intelligence, machine learning, and representation learning are, we
can now move our entire focus to elaborate on deep learning with
further description.
From the previously mentioned definitions of deep learning, two
major characteristics of deep learning can be pointed out, as follows:
A way of experiencing unsupervised and supervised learning of
the feature representation through successive knowledge from
subsequent abstract layers
A model comprising of multiple abstract stages of non-linear
information processing](https://image.slidesharecdn.com/102603-250517024311-fac47e49/85/Deep-Learning-with-Hadoop-1st-Edition-Dipayan-Dev-45-320.jpg)
![Deep learning terminologies
Deep Neural Network (DNN): This can be defined as a
multilayer perceptron with many hidden layers. All the weights
of the layers are fully connected to each other, and receive
connections from the previous layer. The weights are initialized
with either supervised or unsupervised learning.
Recurrent Neural Networks (RNN): RNN is a kind of deep
learning network that is specially used in learning from time
series or sequential data, such as speech, video, and so on. The
primary concept of RNN is that the observations from the
previous state need to be retained for the next state. The recent
hot topic in deep learning with RNN is Long short-term
memory (LSTM).
Deep belief network (DBN): This type of network [9] [10]
[11] can be defined as a probabilistic generative model with
visible and multiple layers of latent variables (hidden). Each
hidden layer possesses a statistical relationship between units in
the lower layer through learning. The more the networks tend
to move to higher layers, the more complex relationship
becomes. This type of network can be productively trained using
greedy layer-wise training, where all the hidden layers are
trained one at a time in a bottom-up fashion.
Boltzmann machine (BM): This can be defined as a network
that is a symmetrically connected, neuron-like unit, which is
capable of taking stochastic decisions about whether to remain
on or off. BMs generally have a simple learning algorithm, which
allows them to uncover many interesting features that represent
complex regularities in the training dataset.
Restricted Boltzmann machine (RBM): RBM, which is a
generative stochastic Artificial Neural Network, is a special type
of Boltzmann Machine. These types of networks have the
capability to learn a probability distribution over a collection of
datasets. An RBM consists of a layer of visible and hidden units,
but with no visible-visible or hidden-hidden connections.](https://image.slidesharecdn.com/102603-250517024311-fac47e49/85/Deep-Learning-with-Hadoop-1st-Edition-Dipayan-Dev-46-320.jpg)
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- 6. Table of Contents Deep Learning with Hadoop Credits About the Author About the Reviewers www.PacktPub.com Why subscribe? Customer Feedback Dedication Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Downloading the color images of this book Errata Piracy Questions 1. Introduction to Deep Learning Getting started with deep learning Deep feed-forward networks Various learning algorithms Unsupervised learning Supervised learning Semi-supervised learning Deep learning terminologies Deep learning: A revolution in Artificial Intelligence Motivations for deep learning The curse of dimensionality The vanishing gradient problem Distributed representation Classification of deep learning networks
- 7. Deep generative or unsupervised models Deep discriminate models Summary 2. Distributed Deep Learning for Large-Scale Data Deep learning for massive amounts of data Challenges of deep learning for big data Challenges of deep learning due to massive volumes of data (first V) Challenges of deep learning from a high variety of data (second V) Challenges of deep learning from a high velocity of data (third V) Challenges of deep learning to maintain the veracity of data (fourth V) Distributed deep learning and Hadoop Map-Reduce Iterative Map-Reduce Yet Another Resource Negotiator (YARN) Important characteristics for distributed deep learning design Deeplearning4j - an open source distributed framework for deep learning Major features of Deeplearning4j Summary of functionalities of Deeplearning4j Setting up Deeplearning4j on Hadoop YARN Getting familiar with Deeplearning4j Integration of Hadoop YARN and Spark for distributed deep learning Rules to configure memory allocation for Spark on Hadoop YARN Summary 3. Convolutional Neural Network Understanding convolution Background of a CNN Architecture overview Basic layers of CNN Importance of depth in a CNN
- 8. Convolutional layer Sparse connectivity Improved time complexity Parameter sharing Improved space complexity Equivariant representations Choosing the hyperparameters for Convolutional layers Depth Stride Zero-padding Mathematical formulation of hyperparameters Effect of zero-padding ReLU (Rectified Linear Units) layers Advantages of ReLU over the sigmoid function Pooling layer Where is it useful, and where is it not? Fully connected layer Distributed deep CNN Most popular aggressive deep neural networks and their configurations Training time - major challenges associated with deep neural networks Hadoop for deep CNNs Convolutional layer using Deeplearning4j Loading data Model configuration Training and evaluation Summary 4. Recurrent Neural Network What makes recurrent networks distinctive from others? Recurrent neural networks(RNNs) Unfolding recurrent computations Advantages of a model unfolded in time Memory of RNNs Architecture Backpropagation through time (BPTT)
- 9. Error computation Long short-term memory Problem with deep backpropagation with time Long short-term memory Bi-directional RNNs Shortfalls of RNNs Solutions to overcome Distributed deep RNNs RNNs with Deeplearning4j Summary 5. Restricted Boltzmann Machines Energy-based models Boltzmann machines How Boltzmann machines learn Shortfall Restricted Boltzmann machine The basic architecture How RBMs work Convolutional Restricted Boltzmann machines Stacked Convolutional Restricted Boltzmann machines Deep Belief networks Greedy layer-wise training Distributed Deep Belief network Distributed training of Restricted Boltzmann machines Distributed training of Deep Belief networks Distributed back propagation algorithm Performance evaluation of RBMs and DBNs Drastic improvement in training time Implementation using Deeplearning4j Restricted Boltzmann machines Deep Belief networks Summary 6. Autoencoders Autoencoder Regularized autoencoders Sparse autoencoders
- 10. Sparse coding Sparse autoencoders The k-Sparse autoencoder How to select the sparsity level k Effect of sparsity level Deep autoencoders Training of deep autoencoders Implementation of deep autoencoders using Deeplearning4j Denoising autoencoder Architecture of a Denoising autoencoder Stacked denoising autoencoders Implementation of a stacked denoising autoencoder using Deeplearning4j Applications of autoencoders Summary 7. Miscellaneous Deep Learning Operations using Hadoop Distributed video decoding in Hadoop Large-scale image processing using Hadoop Application of Map-Reduce jobs Natural language processing using Hadoop Web crawler Extraction of keyword and module for natural language processing Estimation of relevant keywords from a page Summary 1. References
- 11. Deep Learning with Hadoop
- 12. Deep Learning with Hadoop Copyright © 2017 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. First published: February 2017 Production reference: 1130217 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78712-476-9 www.packtpub.com
- 13. Credits Authors Dipayan Dev Copy Editor Safis Editing Reviewers Shashwat Shriparv Wissem EL Khlifi Project Coordinator Shweta H Birwatkar Commissioning Editor Amey Varangaonkar Proofreader Safis Editing Acquisition Editor Divya Poojari Indexer Mariammal Chettiyar Content Development Editor Sumeet Sawant Graphics Tania Dutta Technical Editor Nilesh Sawakhande Production Coordinator Melwyn Dsa
- 14. About the Author Dipayan Dev has completed his M.Tech from National Institute of Technology, Silchar with a first class first and is currently working as a software professional in Bengaluru, India. He has extensive knowledge and experience in non-relational database technologies, having primarily worked with large-scale data over the last few years. His core expertise lies in Hadoop Framework. During his postgraduation, Dipayan had built an infinite scalable framework for Hadoop, called Dr. Hadoop, which got published in top-tier SCI-E indexed journal of Springer (http://link.springer.com/article/10.1631/FITEE.1500015). Dr. Hadoop has recently been cited by Goo Wikipedia in their Apache Hadoop article. Apart from that, he registers interest in a wide range of distributed system technologies, such as Redis, Apache Spark, Elasticsearch, Hive, Pig, Riak, and other NoSQL databases. Dipayan has also authored various research papers and book chapters, which are published by IEEE and top-tier Springer Journals. To know more about him, you can also visit his LinkedIn profile https://www.linkedin.com/in/dipayandev.
- 15. About the Reviewers Shashwat Shriparv has more than 7 years of IT experience. He has worked with various technologies on his career path, such as Hadoop and subprojects, Java, .NET, and so on. He has experience in technologies such as Hadoop, HBase, Hive, Pig, Flume, Sqoop, Mongo, Cassandra, Java, C#, Linux, Scripting, PHP, C++, C, Web technologies, and various real-life use cases in BigData technologies as a developer and administrator. He likes to ride bikes, has interest in photography, and writes blogs when not working. He has worked with companies such as CDAC, Genilok, HCL, UIDAI(Aadhaar), Pointcross; he is currently working with CenturyLink Cognilytics. He is the author of Learning HBase, Packt Publishing, the reviewer of Pig Design Pattern book, Packt Publishing, and the reviewer of Hadoop Real-World Solution cookbook, 2nd edition. I would like to take this opportunity to thank everyone who have somehow made my life better and appreciated me at my best and bared with me and supported me during my bad times. Wissem El Khlifi is the first Oracle ACE in Spain and an Oracle Certified Professional DBA with over 12 years of IT experience. He earned the Computer Science Engineer degree from FST Tunisia, Masters in Computer Science from the UPC Barcelona, and Masters in Big Data Science from the UPC Barcelona. His area of interest include Cloud Architecture, Big Data Architecture, and Big Data Management & Analysis. His career has included the roles of: Java analyst / programmer, Oracle Senior DBA, and big data scientist. He currently works as Senior Big Data and Cloud Architect for Schneider Electric / APC. He writes numerous articles on his website http://www.oracle-class.com and his twitter handle is @orawiss.
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- 18. Dedication To my mother, Dipti Deb and father, Tarun Kumar Deb. And also my elder brother, Tapojit Deb.
- 19. Preface This book will teach you how to deploy large-scale datasets in deep neural networks with Hadoop for optimal performance. Starting with understanding what deep learning is, and what the various models associated with deep neural networks are, this book will then show you how to set up the Hadoop environment for deep learning.
- 20. What this book covers Chapter 1, Introduction to Deep Learning, covers how deep learning has gained its popularity over the last decade and is now growing even faster than machine learning due to its enhanced functionalities. This chapter starts with an introduction of the real- life applications of Artificial Intelligence, the associated challenges, and how effectively Deep learning is able to address all of these. The chapter provides an in-depth explanation of deep learning by addressing some of the major machine learning problems such as, The curse of dimensionality, Vanishing gradient problem, and the likes. To get started with deep learning for the subsequent chapters, the classification of various deep learning networks is discussed in the latter part of this chapter. This chapter is primarily suitable for readers, who are interested to know the basics of deep learning without getting much into the details of individual deep neural networks. Chapter 2, Distributed Deep Learning for Large - Scale Data, explains that big data and deep learning are undoubtedly the two hottest technical trends in recent days. Both of them are critically interconnected and have shown tremendous growth in the past few years. This chapter starts with how deep learning technologies can be furnished with massive amount of unstructured data to facilitate extraction of valuable hidden information out of them. Famous technological companies such as Google, Facebook, Apple, and the like are using this large-scale data in their deep learning projects to train some aggressively deep neural networks in a smarter way. Deep neural networks, however, show certain challenges while dealing with Big data. This chapter provides a detailed explanation of all these challenges. The latter part of the chapter introduces Hadoop, to discuss how deep learning models can be implemented using Hadoop's YARN and its iterative Map- reduce paradigm. The chapter further introduces Deeplearning4j, a popular open source distributed framework for deep learning and explains its various components.
- 21. Chapter 3 , Convolutional Neural Network, introduces Convolutional neural network (CNN), a deep neural network widely used by top technological industries in their various deep learning projects. CNN comes with a vast range of applications in various fields such as image recognition, video recognition, natural language processing, and so on. Convolution, a special type of mathematical operation, is an integral component of CNN. To get started, the chapter initially discusses the concept of convolution with a real-life example. Further, an in-depth explanation of Convolutional neural network is provided by describing each component of the network. To improve the performance of the network, CNN comes with three most important parameters, namely, sparse connectivity, parameter sharing, and equivariant representation. The chapter explains all of these to get a better grip on CNN. Further, CNN also possesses few crucial hyperparameters, which help in deciding the dimension of output volume of the network. A detailed discussion along with the mathematical relationship among these hyperparameters can be found in this chapter. The latter part of the chapter focuses on distributed convolutional neural networks and shows its implementation using Hadoop and Deeplearning4j. Chapter 4, Recurrent Neural Network, explains that it is a special type of neural network that can work over long sequences of vectors to produce different sequences of vectors. Recently, they have become an extremely popular choice for modeling sequences of variable length. RNN has been successfully implemented for various applications such as speech recognition, online handwritten recognition, language modeling, and the like. The chapter provides a detailed explanation of the various concepts of RNN by providing essential mathematical relations and visual representations. RNN possesses its own memory to store the output of the intermediate hidden layer. Memory is the core component of the recurrent neural network, which has been discussed in this chapter with an appropriate block diagram. Moreover, the limitations of uni- directional recurrent neural networks are provided, and to overcome the same, the concept of bidirectional recurrent neural network (BRNN) is introduced. Later, to address the problem of vanishing
- 22. gradient, introduced in chapter 1, a special unit of RNN, called Long short-term Memory (LSTM) is discussed. In the end, the implementation of distributed deep recurrent neural network with Hadoop is shown with Deeplearning4j. Chapter 5 , Restricted Boltzmann Machines, covers both the models discussed in chapters 3 and 4 and explains that they are discriminative models. A generative model called Restricted Boltzmann machine (RBM) is discussed in chapter 5. RBM is capable of randomly producing visible data values when hidden parameters are supplied to it. The chapter starts with introducing the concept of an Energy-based model, and explains how Restricted Boltzmann machines are related to it. Furthermore, the discussion progresses towards a special type of RBM known as Convolutional Restricted Boltzmann machine, which is a combination of both Convolution and Restricted Boltzmann machines, and facilitates in the extraction of the features of high dimensional images. Deep Belief networks (DBN), a widely used multilayer network composed of several Restricted Boltzmann machines gets introduced in the latter part of the chapter. This part also discusses how DBN can be implemented in a distributed environment using Hadoop. The implementation of RBM as well as distributed DBN using Deeplearning4j is discussed in the end of the chapter. Chapter 6, Autoencoders, introduces one more generative model called autoencoder, which is generally used for dimensionality reduction, feature learning, or extraction. The chapter starts with explaining the basic concept of autoencoder and its generic block diagram. The core structure of an autoencoder is basically divided into two parts, encoder and decoder. The encoder maps the input to the hidden layer, whereas the decoder maps the hidden layer to the output layer. The primary concern of a basic autoencoder is to copy certain aspects of the input layer to the output layer. The next part of the chapter discusses a type of autoencoder called sparse autoencoder, which is based on the distributed sparse representation of the hidden layer. Going further, the concept of deep autoencoder, comprising multiple encoders and decoders is explained in-depth with an appropriate example and block diagram. As we proceed,
- 23. denoising autoencoder and stacked denoising autoencoder are explained in the latter part of the chapter. In conclusion, chapter 6 also shows the implementation of stacked denoising autoencoder and deep autoencoder in Hadoop using Deeplearning4j. Chapter 7 , Miscellaneous Deep Learning Operations using Hadoop, focuses, mainly,on the design of three most commonly used machine learning applications in distributed environment. The chapter discusses the implementation of large-scale video processing, large-scale image processing, and natural language processing (NLP) with Hadoop. It explains how the large-scale video and image datasets can be deployed in Hadoop Distributed File System (HDFS) and processed with Map-reduce algorithm. For NLP, an in-depth explanation of the design and implementation is provided at the end of the chapter.
- 24. What you need for this book We expect all the readers of this book to have some background on computer science. This book mainly talks on different deep neural networks, their designs and applications with Deeplearning4j. To extract the most out of the book, the readers are expected to know the basics of machine learning, linear algebra, probability theory, the concepts of distributed systems and Hadoop. For the implementation of deep neural networks with Hadoop, Deeplearning4j has been extensively used throughout this book. Following is the link for everything you need to run Deeplearning4j: https://deeplearning4j.org/quickstart
- 25. Who this book is for If you are a data scientist who wants to learn how to perform deep learning on Hadoop, this is the book for you. Knowledge of the basic machine learning concepts and some understanding of Hadoop is required to make the best use of this book.
- 26. Conventions 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 .build() function is used to build the layer." A block of code is set as follows: public static final String DATA_URL = "http://ai.stanford.edu/~amaas/data/sentiment/*"; When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold: MultiLayerNetwork model = new MultiLayerNetwork(getConfiguration()); Model.init(); 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 simple words, any neural network with two or more layers (hidden) is defined as a deep feed- forward network or feed-forward neural network." Note Warnings or important notes appear in a box like this. Tip Tips and tricks appear like this.
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- 31. Chapter 1. Introduction to Deep Learning "By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it." --Eliezer Yudkowsky Ever thought, why it is often difficult to beat the computer in chess, even for the best players of the game? How Facebook is able to recognize your face amid hundreds of millions of photos? How can your mobile phone recognize your voice, and redirect the call to the correct person, from hundreds of contacts listed? The primary goal of this book is to deal with many of those queries, and to provide detailed solutions to the readers. This book can be used for a wide range of reasons by a variety of readers, however, we wrote the book with two main target audiences in mind. One of the primary target audiences is undergraduate or graduate university students learning about deep learning and Artificial Intelligence; the second group of readers are the software engineers who already have a knowledge of big data, deep learning, and statistical modeling, but want to rapidly gain knowledge of how deep learning can be used for big data and vice versa. This chapter will mainly try to set a foundation for the readers by providing the basic concepts, terminologies, characteristics, and the major challenges of deep learning. The chapter will also put forward the classification of different deep network algorithms, which have been widely used by researchers over the last decade. The following are the main topics that this chapter will cover: Getting started with deep learning Deep learning terminologies Deep learning: A revolution in Artificial Intelligence Classification of deep learning networks Ever since the dawn of civilization, people have always dreamt of building artificial machines or robots which can behave and work exactly like human beings. From the Greek mythological characters
- 32. to the ancient Hindu epics, there are numerous such examples, which clearly suggest people's interest and inclination towards creating and having an artificial life. During the initial computer generations, people had always wondered if the computer could ever become as intelligent as a human being! Going forward, even in medical science, the need of automated machines has become indispensable and almost unavoidable. With this need and constant research in the same field, Artificial Intelligence (AI) has turned out to be a flourishing technology with various applications in several domains, such as image processing, video processing, and many other diagnosis tools in medical science too. Although there are many problems that are resolved by AI systems on a daily basis, nobody knows the specific rules for how an AI system is programmed! A few of the intuitive problems are as follows: Google search, which does a really good job of understanding what you type or speak As mentioned earlier, Facebook is also somewhat good at recognizing your face, and hence, understanding your interests Moreover, with the integration of various other fields, for example, probability, linear algebra, statistics, machine learning, deep learning, and so on, AI has already gained a huge amount of popularity in the research field over the course of time. One of the key reasons for the early success of AI could be that it basically dealt with fundamental problems for which the computer did not require a vast amount of knowledge. For example, in 1997, IBM's Deep Blue chess-playing system was able to defeat the world champion Garry Kasparov [1]. Although this kind of achievement at that time can be considered significant, it was definitely not a burdensome task to train the computer with only the limited number of rules involved in chess! Training a system with a fixed and limited number of rules is termed as hard-coded knowledge of the computer. Many Artificial Intelligence projects have undergone this hard-coded knowledge about the various aspects of the world in many traditional languages. As time progresses, this hard-coded
- 33. knowledge does not seem to work with systems dealing with huge amounts of data. Moreover, the number of rules that the data was following also kept changing in a frequent manner. Therefore, most of the projects following that system failed to stand up to the height of expectation. The setbacks faced by this hard-coded knowledge implied that those artificial intelligence systems needed some way of generalizing patterns and rules from the supplied raw data, without the need for external spoon-feeding. The proficiency of a system to do so is termed as machine learning. There are various successful machine learning implementations which we use in our daily life. A few of the most common and important implementations are as follows: Spam detection: Given an e-mail in your inbox, the model can detect whether to put that e-mail in spam or in the inbox folder. A common naive Bayes model can distinguish between such e- mails. Credit card fraud detection: A model that can detect whether a number of transactions performed at a specific time interval are carried out by the original customer or not. One of the most popular machine learning models, given by Mor-Yosef et al in 1990, used logistic regression, which could recommend whether caesarean delivery was needed for the patient or not! There are many such models which have been implemented with the help of machine learning techniques. Figure 1.1: The figure shows the example of different types of representation. Let's say we want to train the machine to detect
- 34. some empty spaces in between the jelly beans. In the image on the right side, we have sparse jelly beans, and it would be easier for the AI system to determine the empty parts. However, in the image on the left side, we have extremely compact jelly beans, and hence, it will be an extremely difficult task for the machine to find the empty spaces. Images sourced from USC-SIPI image database A large portion of performance of the machine learning systems depends on the data fed to the system. This is called representation of the data. All the information related to the representation is called the feature of the data. For example, if logistic regression is used to detect a brain tumor in a patient, the AI system will not try to diagnose the patient directly! Rather, the concerned doctor will provide the necessary input to the systems according to the common symptoms of that patient. The AI system will then match those inputs with the already received past inputs which were used to train the system. Based on the predictive analysis of the system, it will provide its decision regarding the disease. Although logistic regression can learn and decide based on the features given, it cannot influence or modify the way features are defined. Logistic regression is a type of regression model where the dependent variable has a limited number of possible values based on the independent variable, unlike linear regression. So, for example, if that model was provided with a caesarean patient's report instead of the brain tumor patient's report, it would surely fail to predict the correct outcome, as the given features would never match with the trained data. These dependencies of the machine learning systems on the representation of the data are not really unknown to us! In fact, most of our computer theory performs better based on how the data are represented. For example, the quality of a database is considered based on how the schema is designed. The execution of any database query, even on a thousand or a million lines of data, becomes extremely fast if the table is indexed properly. Therefore, the dependency of the data representation of the AI systems should not surprise us.
- 35. There are many such examples in daily life too, where the representation of the data decides our efficiency. To locate a person amidst 20 people is obviously easier than to locate the same person in a crowd of 500 people. A visual representation of two different types of data representation is shown in the preceding Figure 1.1. Therefore, if the AI systems are fed with the appropriate featured data, even the hardest problems could be resolved. However, collecting and feeding the desired data in the correct way to the system has been a serious impediment for the computer programmer. There can be numerous real-time scenarios where extracting the features could be a cumbersome task. Therefore, the way the data are represented decides the prime factors in the intelligence of the system. Note Finding cats amidst a group of humans and cats can be extremely complicated if the features are not appropriate. We know that cats have tails; therefore, we might like to detect the presence of tails as a prominent feature. However, given the different tail shapes and sizes, it is often difficult to describe exactly how a tail will look like in terms of pixel values! Moreover, tails could sometimes be confused with the hands of humans. Also, overlapping of some objects could omit the presence of a cat's tail, making the image even more complicated. From all the above discussions, it can be concluded that the success of AI systems depends mainly on how the data are represented. Also, various representations can ensnare and cache the different explanatory factors of all the disparities behind the data. Representation learning is one of the most popular and widely practiced learning approaches used to cope with these specific problems. Learning the representations of the next layer from the existing representation of data can be defined as representation learning. Ideally, all representation learning algorithms have this advantage of learning representations, which capture the underlying
- 36. factors, a subset that might be applicable for each particular sub- task. A simple illustration is given in the following Figure 1.2: Figure 1.2: The figure illustrates representation learning. The middle layers are able to discover the explanatory factors (hidden layers, in blue rectangular boxes). Some of the factors explain each task's target, whereas some explain the inputs However, dealing with extracting some high-level data and features from a massive amount of raw data, which requires some sort of human-level understanding, has shown its limitations. There can be many such examples: Differentiating the cry of two similar age babies. Identifying the image of a cat's eye at both day and night time. This becomes clumsy, because a cat's eyes glow at night unlike during the daytime. In all these preceding edge cases, representation learning does not appear to behave exceptionally, and shows deterrent behavior. Deep learning, a sub-field of machine learning, can rectify this major problem of representation learning by building multiple levels of representations or learning a hierarchy of features from a series of other simple representations and features [2] [8].
- 37. Figure 1.3: The figure shows how a deep learning system can represent the human image by identifying various combinations such as corners and contours, which can be defined in terms of edges. Image reprinted with permission from Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, published by The MIT Press The preceding Figure 1.3 shows an illustration of a deep learning model. It is generally a cumbersome task for the computer to decode the meaning of raw unstructured input data, as represented by this image, as a collection of different pixel values. A mapping function, which will convert the group of pixels to identify the image, is ideally difficult to achieve. Also, to directly train the computer for these kinds of mapping is almost insuperable. For these types of tasks, deep learning resolves the difficulty by creating a series of subsets of mappings to reach the desired output. Each subset of mappings corresponds to a different set of layer of the model. The input contains the variables that one can observe, and hence , are represented in the visible layers. From the given input we can
- 38. incrementally extract the abstract features of the data. As these values are not available or visible in the given data, these layers are termed as hidden layers. In the image, from the first layer of data, the edges can easily be identified just by a comparative study of the neighboring pixels. The second hidden layer can distinguish the corners and contours from the first hidden layer's description of the edges. From this second hidden layer, which describes the corners and contours, the third hidden layer can identify the different parts of the specific objects. Ultimately, the different objects present in the image can be distinctly detected from the third layer. Deep learning started its journey exclusively in 2006, Hinton et al. in 2006[2]; also Bengio et al. in 2007[3] initially focused on the MNIST digit classification problem. In the last few years, deep learning has seen major transitions from digits to object recognition in natural images. Apart from this, one of the major breakthroughs was achieved by Krizhevsky et al. in 2012 [4] using the ImageNet dataset. The scope of this book is mainly limited to deep learning, so before diving into it directly, the necessary definitions of deep learning should be discussed. Many researchers have defined deep learning in many ways, and hence, in the last 10 years, it has gone through many definitions too! The following are few of the widely accepted definitions: As noted by GitHub, deep learning is a new area of machine learning research, which has been introduced with the objective of moving machine learning closer to one of its original goals: Artificial Intelligence. Deep learning is about learning multiple levels of representation and abstraction, which help to make sense of data such as images, sounds, and texts. As recently updated by Wikipedia, deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in the data by using a deep graph with multiple processing layers, composed of multiple linear and non-linear transformations.
- 39. As the definitions suggest, deep learning can also be considered as a special type of machine learning. Deep learning has achieved immense popularity in the field of data science with its ability to learn complex representation from various simple features. To have an in-depth grip on deep learning, we have listed out a few terminologies which will be frequently used in the upcoming chapters. The next topic of this chapter will help you to lay a foundation for deep learning by providing various terminologies and important networks used for deep learning. Getting started with deep learning To understand the journey of deep learning in this book, one must know all the terminologies and basic concepts of machine learning. However, if you already have enough insight into machine learning and related terms, you should feel free to ignore this section and jump to the next topic of this chapter. Readers who are enthusiastic about data science, and want to learn machine learning thoroughly, can follow Machine Learning by Tom M. Mitchell (1997) [5] and Machine Learning: a Probabilistic Perspective (2012) [6]. Note Neural networks do not perform miracles. But, used sensibly, they can produce some amazing results. Deep feed-forward networks Neural networks can be recurrent as well as feed-forward. Feed- forward networks do not have any loop associated in their graph, and are arranged in a set of layers. A network with many layers is said to be a deep network. In simple words, any neural network with two or more layers (hidden) is defined as a deep feed-forward network or feed-forward neural network. Figure 1.4 shows a generic representation of a deep feed-forward neural network. Deep feed-forward networks work on the principle that with an increase in depth, the network can also execute more sequential
- 40. instructions. Instructions in sequence can offer great power, as these instructions can point to the earlier instruction. The aim of a feed-forward network is to generalize some function f. For example, classifier y=f(x) maps from input x to category y. A deep feed-forward network modified the mapping, y=f(x; α), and learns the value of the parameter α, which gives the most appropriate value of the function. The following Figure 1.4 shows a simple representation of the deep-forward network, to provide the architectural difference with the traditional neural network. Note A deep neural network is a feed-forward network with many hidden layers. Figure 1.4: Figure shows the representation of a shallow and deep feed-forward network Various learning algorithms Datasets are considered to be the building blocks of a learning process. A dataset can be defined as a collection of interrelated sets of data, which is comprised of separate entities, but which can be used as a single entity depending on the use-case. The individual data elements of a dataset are called data points. The following Figure 1.5 gives the visual representation of the various data points collected from a social network analysis:
- 41. Figure 1.5: Image shows the scattered data points of social network analysis. Image sourced from Wikipedia Unlabeled data: This part of data consists of the human- generated objects, which can be easily obtained from the surroundings. Some of the examples are X-rays, log file data, news articles, speech, videos, tweets, and so on. Labelled data: Labelled data are normalized data from a set of unlabeled data. These types of data are usually well formatted, classified, tagged, and easily understandable by human beings for further processing. From the top-level understanding, machine learning techniques can be classified as supervised and unsupervised learning, based on how their learning process is carried out. Unsupervised learning In unsupervised learning algorithms, there is no desired output from the given input datasets. The system learns meaningful properties
- 42. and features from its experience during the analysis of the dataset. In deep learning, the system generally tries to learn from the whole probability distribution of the data points. There are various types of unsupervised learning algorithms, which perform clustering. To explain in simple words, clustering means separating the data points among clusters of similar types of data. However, with this type of learning, there is no feedback based on the final output, that is, there won't be any teacher to correct you! Figure 1.6 shows a basic overview of unsupervised clustering: Figure 1.6: Figures shows a simple representation of unsupervised clustering A real life example of an unsupervised clustering algorithm is Google News. When we open a topic under Google News, it shows us a number of hyper-links redirecting to several pages. Each of these topics can be considered as a cluster of hyper-links that point to independent links. Supervised learning In supervised learning, unlike unsupervised learning, there is an expected output associated with every step of the experience. The system is given a dataset, and it already knows how the desired output will look, along with the correct relationship between the input and output of every associated layer. This type of learning is often used for classification problems. The following visual representation is given in Figure 1.7:
- 43. Figure 1.7: Figure shows the classification of data based on supervised learning Real-life examples of supervised learning include face detection, face recognition, and so on. Although supervised and unsupervised learning look like different identities, they are often connected to each other by various means. Hence, the fine line between these two learnings is often hazy to the student fraternity. The preceding statement can be formulated with the following mathematical expression: The general product rule of probability states that for an n number of datasets n ε ℝt ,the joint distribution can be fragmented as follows: The distribution signifies that the appeared unsupervised problem can be resolved by t number of supervised problems. Apart from this, the conditional probability of p (k | n), which is a supervised problem, can be solved using unsupervised learning algorithms to experience the joint distribution of p (n, k).
- 44. Although these two types are not completely separate identities, they often help to classify the machine learning and deep learning algorithms based on the operations performed. In generic terms, cluster formation, identifying the density of a population based on similarity, and so on are termed as unsupervised learning, whereas structured formatted output, regression, classification, and so on are recognized as supervised learning. Semi-supervised learning As the name suggests, in this type of learning both labelled and unlabeled data are used during the training. It's a class of supervised learning which uses a vast amount of unlabeled data during training. For example, semi-supervised learning is used in a Deep belief network (explained later), a type of deep network where some layers learn the structure of the data (unsupervised), whereas one layer learns how to classify the data (supervised learning). In semi-supervised learning, unlabeled data from p (n) and labelled data from p (n, k) are used to predict the probability of k, given the probability of n, or p (k | n).
- 45. Figure 1.8: Figure shows the impact of a large amount of unlabelled data during the semi-supervised learning technique. Figure obtained from Wikipedia In the preceding Figure 1.8, at the top it shows the decision boundary that the model uses after distinguishing the white and black circles. The figure at the bottom displays another decision boundary, which the model embraces. In that dataset, in addition to two different categories of circles, a collection of unlabeled data (grey circle) is also annexed. This type of training can be viewed as creating the cluster, and then marking those with the labelled data, which moves the decision boundary away from the high-density data region. The preceding Figure 1.8 depicts the illustration of semi-supervised learning. You can refer to Chapelle et al.'s book [7] to know more about semi-supervised learning methods. So, as you have already got a foundation in what Artificial Intelligence, machine learning, and representation learning are, we can now move our entire focus to elaborate on deep learning with further description. From the previously mentioned definitions of deep learning, two major characteristics of deep learning can be pointed out, as follows: A way of experiencing unsupervised and supervised learning of the feature representation through successive knowledge from subsequent abstract layers A model comprising of multiple abstract stages of non-linear information processing
- 46. Deep learning terminologies Deep Neural Network (DNN): This can be defined as a multilayer perceptron with many hidden layers. All the weights of the layers are fully connected to each other, and receive connections from the previous layer. The weights are initialized with either supervised or unsupervised learning. Recurrent Neural Networks (RNN): RNN is a kind of deep learning network that is specially used in learning from time series or sequential data, such as speech, video, and so on. The primary concept of RNN is that the observations from the previous state need to be retained for the next state. The recent hot topic in deep learning with RNN is Long short-term memory (LSTM). Deep belief network (DBN): This type of network [9] [10] [11] can be defined as a probabilistic generative model with visible and multiple layers of latent variables (hidden). Each hidden layer possesses a statistical relationship between units in the lower layer through learning. The more the networks tend to move to higher layers, the more complex relationship becomes. This type of network can be productively trained using greedy layer-wise training, where all the hidden layers are trained one at a time in a bottom-up fashion. Boltzmann machine (BM): This can be defined as a network that is a symmetrically connected, neuron-like unit, which is capable of taking stochastic decisions about whether to remain on or off. BMs generally have a simple learning algorithm, which allows them to uncover many interesting features that represent complex regularities in the training dataset. Restricted Boltzmann machine (RBM): RBM, which is a generative stochastic Artificial Neural Network, is a special type of Boltzmann Machine. These types of networks have the capability to learn a probability distribution over a collection of datasets. An RBM consists of a layer of visible and hidden units, but with no visible-visible or hidden-hidden connections.
- 47. Convolutional neural networks: Convolutional neural networks are part of neural networks; the layers are sparsely connected to each other and to the input layer. Each neuron of the subsequent layer is responsible for only a part of the input. Deep convolutional neural networks have accomplished some unmatched performance in the field of location recognition, image classification, face recognition, and so on. Deep auto-encoder: A deep auto-encoder is a type of auto- encoder that has multiple hidden layers. This type of network can be pre-trained as a stack of single-layered auto-encoders. The training process is usually difficult: first, we need to train the first hidden layer to restructure the input data, which is then used to train the next hidden layer to restructure the states of the previous hidden layer, and so on. Gradient descent (GD): This is an optimization algorithm used widely in machine learning to determine the coefficient of a function (f), which reduces the overall cost function. Gradient descent is mostly used when it is not possible to calculate the desired parameter analytically (for example, linear algebra), and must be found by some optimization algorithm. In gradient descent, weights of the model are incrementally updated with every single iteration of the training dataset (epoch). The cost function, J (w), with the sum of the squared errors can be written as follows: The direction of magnitude of the weight update is calculated by taking a step in the reverse direction of the cost gradient, as follows: In the preceding equation, η is the learning rate of the network. Weights are updated incrementally after every epoch with the
- 48. following rule: for one or more epochs, for each weight i, wi:= w + ∆wi end end Popular examples that can be optimized using gradient descent are Logistic Regression and Linear Regression. Stochastic Gradient Descent (SGD): Various deep learning algorithms, which operated on a large amount of datasets, are based on an optimization algorithm called stochastic gradient descent. Gradient descent performs well only in the case of small datasets. However, in the case of very large-scale datasets, this approach becomes extremely costly . In gradient descent, it takes only one single step for one pass over the entire training dataset; thus, as the dataset's size tends to increase, the whole algorithm eventually slows down. The weights are updated at a very slow rate; hence, the time it takes to converge to the global cost minimum becomes protracted. Therefore, to deal with such large-scale datasets, a variation of gradient descent called stochastic gradient descent is used. Unlike gradient descent, the weight is updated after each iteration of the training dataset, rather than at the end of the entire dataset. until cost minimum is reached for each training sample j: for each weight i wi:= w + ∆wi end end end
- 49. In the last few years, deep learning has gained tremendous popularity, as it has become a junction for research areas of many widely practiced subjects, such as pattern recognition, neural networks, graphical modelling, machine learning, and signal processing. The other important reasons for this popularity can be summarized by the following points: In recent years, the ability of GPU (Graphical Processing Units) has increased drastically The size of data sizes of the dataset used for training purposes has increased significantly Recent research in machine learning, data science, and information processing has shown some serious advancements Detailed descriptions of all these points will be provided in an upcoming topic in this chapter.
- 50. Random documents with unrelated content Scribd suggests to you:
- 51. THE TALES OF THE HEPTAMERON OF MARGARET, QUEEN OF NAVARRE IN FIVE VOLUMES
- 52. VOLUME THE FOURTH CONTENTS FOURTH DAY. PROLOGUE. TALE XXXI. TALE XXXII. TALE XXXIII. TALE XXXIV. TALE XXXV. TALE XXXVI. TALE XXXVII. TALE XXXVIII. TALE XXXIX. TALE XL. FIFTH DAY. PROLOGUE. TALE XLI. TALE XLII. TALE XLIII. TALE XLIV.(A). TALE XLIV. (B). TALE XLV. TALE XLVI. (A). TALE XLVI.(B). TALE XLVII. TALE XLVIII.
- 53. TALE XLIX. TALE L. APPENDIX. A. (Tale XXXVI., Page 63.) ILLUSTRATIONS Frontispiece Titlepage 007a.jpg the Wicked Friar Captured 007.jpg Page Image 0016.jpg Tailpiece 017a.jpg Bernage Observing the German Lady’s Strange Penance 017.jpg Page Image 028.jpg Tailpiece 029a.jpg the Execution of The Wicked Priest and his Sister 029.jpg Page Image 037.jpg Tailpiece 039a.jpg the Grey Friar Imploring The Butcher to Spare his Life 039.jpg Page Image 047.jpg Tailpiece 049a.jpg the Lady Embracing The Supposed Friar 049.jpg Page Image 062.jpg Tailpiece 063a.jpg the Clerk Entreating Forgiveness of The President 063.jpg Page Image 072.jpg Tailpiece 073a.jpg the Lady of Loué Bringing Her Husband The Basin Of Water 073.jpg Page Image
- 54. 081.jpg Tailpiece 083a.jpg the Lady of Tours Questioning Her Husband’s Mistress 083.jpg Page Image 088.jpg Tailpiece 089a.jpg the Lord of Grignaulx Catching The Pretended Ghost 089.jpg Page Image 094.jpg Tailpiece 095a.jpg the Count of Jossebelin Murdering his Sister’s Husband 095.jpg Page Image 109.jpg Tailpiece 115a.jpg the Beating of The Wicked Grey Friar 115.jpg Page Image 122.jpg Tailpiece 123a.jpg the Girl Refusing The Gift of The Young Prince 123.jpg Page Image 142.jpg Tailpiece 143a.jpg Jambicque Repudiating Her Lover 143.jpg Page Image 155.jpg Tailpiece 157.jpg Page Image 162.jpg Tailpiece 163a.jpg the Lovers Returning from Their Meeting in The Garden 163.jpg Page Image 176.jpg Tailpiece 177a.jpg the Man of Tours and his Serving-maid in The Snow 177.jpg Page Image 186.jpg Tailpiece 187.jpg Page Image 193.jpg Tailpiece 195a.jpg the Young Man Beating his Wife
- 55. 195.jpg Page Image 201.jpg Tailpiece 203a.jpg the Gentleman Reproaching his Friend for His Jealousy 203.jpg Page Image 211.jpg Tailpiece 213a.jpg the Grey Friars Caught and Punished 213.jpg Page Image 218.jpg Tailpiece 219a.jpg the Countess Facing Her Lovers 219.jpg Page Image 232.jpg Tailpiece 233a.jpg the Lady Killing Herself on The Death of Her Lover 233.jpg Page Image 240.jpg Tailpiece DETAILED CONTENTS OF VOLUME IV. FOURTH DAY. Prologue Tale XXXI. Punishment of the wickedness of a Friar who sought to lie with a gentleman’s wife. Tale XXXII. How an ambassador of Charles VIII., moved by the repentance of a German lady, whom her husband compelled to drink out of her lover’s skull, reconciled husband and wife together. Tale XXXIII. The hypocrisy of a priest who, under the cloak of sanctity, had lain with his own sister, is discovered and punished by the
- 56. wisdom of the Count of Angoulême. Tale XXXIV. The terror of two Friars who believed that a butcher intended to murder them, whereas the poor man was only speaking of his Pigs. Tale XXXV. How a husband’s prudence saves his wife from the risks she incurred while thinking to yield to merely a spiritual love. Tale XXXVI. The story of the President of Grenoble, who saves the honour of his house by poisoning his wife with a salad. Tale XXXVII. How the Lady of Loué regained her husband’s affection. Tale XXXVIII. The kindness of a townswoman of Tours to a poor farm-woman who is mistress to her husband, makes the latter so ashamed of his faithlessness that he returns to his wife. Tale XXXIX. How the Lord of Grignaulx rid one of his houses of a pretended ghost. Tale XL. The unhappy history of the Count de Jossebelin’s sister, who shut herself up in a hermitage because her brother caused her husband to be slain. FIFTH DAY. Prologue Tale XLI. Just punishment of a Grey Friar for the unwonted penance that he would have laid upon a maiden.
- 57. Tale XLII. The virtuous resistance made by a young woman of Touraine causes a young Prince that is in love with her, to change his desire to respect, and to bestow her honourably in marriage. Tale XLIII. How a little chalk-mark revealed the hypocrisy of a lady called Jambicque, who was wont to hide the pleasures she indulged in, beneath the semblance of austerity. Tale XLIV. (A). Through telling the truth, a Grey Friar receives as alms from the Lord of Sedan two pigs instead of one. Tale XLIV. (B). Honourable conduct of a young citizen of Paris, who, after suddenly enjoying his sweetheart, at last happily marries. Tale XLV. Cleverness of an upholsterer of Touraine, who, to hide that he has given the Innocents to his serving-maid, contrives to give them afterwards to his wife. Tale XLVI. (A). Wicked acts of a Grey Friar of Angoulême called De Vale, who fails in his purpose with the wife of the Judge of the Exempts, but to whom a mother in blind confidence foolishly abandons her daughter. Tale XLVI. (B). Sermons of the Grey Friar De Vallès, at first against and afterwards on behalf of husbands that beat their wives. Tale XLVII. The undeserved jealousy of a gentleman of Le Perche towards another gentleman, his friend, leads the latter to deceive him. Tale XLVIII. Wicked act of a Grey Friar of Perigord, who, while a husband was dancing at his wedding, went and took his place with
- 58. the bride. Tale XLIX. Story of a foreign Countess, who, not content with having King Charles as her lover, added to him three lords, to wit, Astillon, Durassier and Valnebon. Tale L. Melancholy fortune of Messire John Peter, a gentleman of Cremona, who dies just when he is winning the affection of the lady he loves. Appendix to Vol. IV. THE TALES OF THE HEPTAMERON OF MARGARET, QUEEN OF NAVARRE IN FIVE VOLUMES
- 59. VOLUME THE FIFTH CONTENTS SIXTH DAY. PROLOGUE. TALE LI. TALE LII. TALE LIII. TALE LIV. TALE LV. TALE LVI. TALE LVII. TALE LVIII. TALE LIX. TALE LX. SEVENTH DAY. PROLOGUE. TALE LXI. TALE LXII. TALE LXIII. TALE LXIV. TALE LXV. TALE LXVI. TALE LXVII.
- 60. TALE LXVIII. TALE LXIX. TALE LXX. EIGHTH DAY. PROLOGUE. TALE LXXI. TALE LXXII. APPENDIX. THE SUPPOSED NARRATORS OF THE HEPTAMERON TALES. BIBLIOGRAPHY. ILLUSTRATIONS Frontispiece Titlepage 005a.jpg the Duke of Urbino Sending The Maiden to Prison for Carrying Messages 005.jpg Page Image 014.jpg Tailpiece 015a.jpg the Gentleman and his Friend Annoyed by The Smell of Sugar 015.jpg Page Image 022.jpg Tailpiece 023a.jpg the Lord Des Cheriots Flying from The Prince’s Servant 023.jpg Page Image 036.jpg Tailpiece 037a.jpg the Lady Watching The Shadow Faces Kissing 037.jpg Page Image 042.jpg Tailpiece
- 61. 043a.jpg the Servant Selling The Horse With The Cat 043.jpg Page Image 049.jpg Tailpiece 051a.jpg the Grey Friar Introducing his Comrade to The Lady and Her Daughter 051.jpg Page Image 061.jpg Tailpiece 063a.jpg the English Lord Seizing The Lady’s Glove 063.jpg Page Image 070.jpg Tailpiece 071a. The Gentleman Mocked by The Ladies when Returning from The False Tryst 071.jpg Page Image 078.jpg Tailpiece 079a. The Lady Discovering Her Husband With The Waiting-woman 079.jpg Page Image 090.jpg Tailpiece 091a. The Chanter of Blois Delivering his Mistress from The Grave 091.jpg Page Image 099.jpg Tailpiece 105a. The Lady Returning to Her Lover, The Canon of Autun 105.jpg Page Image 117.jpg Tailpiece 119a. The Gentleman’s Spur Catching in The Sheet 119.jpg Page Image 124.jpg Tailpiece 125a. The King Asking The Young Lord to Join his Banquet 125.jpg Page Image 132.jpg Tailpiece 133a. The Lady Swooning in The Arms of The Gentleman Of Valencia Who Had Become a Monk
- 62. 133.jpg Page Image 141.jpg Tailpiece 143a. The Old Woman Startled by The Waking of The Soldier 143.jpg Page Image 147.jpg Tailpiece 149a. The Old Serving-woman Explaining Her Mistake To The Duke and Duchess of Vendôme 149.jpg Page Image 154.jpg Tailpiece 155a. The Wife Reading to Her Husband on The Desert Island 155.jpg Page Image 161.jpg Tailpiece 163a. The Apothecary’s Wife Giving The Dose of Cantharides To Her Husband 163.jpg Page Image 168.jpg Tailpiece 169a. The Wife Discovering Her Husband in The Hood Of Their Serving-maid 169.jpg Page Image 174.jpg Tailpiece 175a. The Gentleman Killing Himself on The Death of his Mistress 175.jpg Page Image 213.jpg Tailpiece 219a. The Saddler’s Wife Cured by The Sight of Her Husband Caressing the Serving-maid 219.jpg Page Image 224.jpg Tailpiece 225a. The Monk Conversing With The Nun While Shrouding A Dead Body 225.jpg Page Image 232.jpg Tailpiece
- 63. DETAILED CONTENTS OF VOLUME V. SIXTH DAY. Prologue Tale LI. Cruelty of the Duke of Urbino, who, contrary to the promise he had given to the Duchess, hanged a poor lady that had consented to convey letters to his son’s sweetheart, the sister of the Abbot of Farse. Tale LII. Merry trick played by the varlet of an apothecary at Alençon on the Lord de la Tirelière and the lawyer Anthony Bacheré, who, thinking to breakfast at his expense, find that they have stolen from him something very different to a loaf of sugar. Tale LIII. Story of the Lady of Neufchâtel, a widow at the Court of Francis I., who, through not admitting that she has plighted her troth to the Lord des Cheriots, plays him an evil trick through the means of the Prince of Belhoste. Tale LIV. Merry adventure of a serving-woman and a gentleman named Thogas, whereof his wife has no suspicion. Tale LV. The widow of a merchant of Saragossa, not wishing to lose the value of a horse, the price of which her husband had ordered to be given to the poor, devises the plan of selling the horse for one ducat only, adding, however, to the bargain a cat at ninety- nine. Tale LVI. Notable deception practised by an old Grey Friar of Padua, who, being charged by a widow to find a husband for her daughter, did, for the sake of getting the dowry, cause her to marry a young Grey Friar, his comrade, whose condition, however, was before long discovered. Tale LVII. Singular behaviour of an English lord, who is content merely to keep and wear upon his doublet the glove of a lady whom he loves.
- 64. Tale LVIII. A lady at the Court of Francis I., wishing to prove that she has no commerce with a certain gentleman who loves her, gives him a pretended tryst and causes him to pass for a thief. Tale LIX. Story of the same lady, who, learning that her husband is in love with her waiting-woman, contrives to surprise him and impose her own terms upon him. Tale LX. A man of Paris, thinking his wife to be well and duly deceased, marries again, but at the end of fifteen years is forced to take his first wife back, although she has been living meantime with one of the chanters of Louis XII. SEVENTH DAY. Prologue Tale LXI. Great kindness of a husband, who consents to take back his wife twice over, spite of her wanton love for a Canon of Autun. Tale LXII. How a lady, while telling a story as of another, let her tongue trip in such a way as to show that what she related had happened to herself. Tale LXIII. How the honourable behaviour of a young lord, who feigns sickness in order to be faithful to his wife, spoils a party in which he was to have made one with the King, and in this way saves the honour of three maidens of Paris. Tale LXIV. Story of a gentleman of Valencia in Spain, whom a lady drove to such despair that he became a monk, and whom afterwards she strove in vain to win back to herself. Tale LXV. Merry mistake of a worthy woman, who in the church of St. John of Lyons mistakes a sleeping soldier for one of the statues on a tomb, and sets a lighted candle on his forehead. Tale LXVI. How an old serving-woman, thinking to surprise a Prothonotary with a lady, finds herself insulting Anthony de Bourbon and his wife Jane d’Albret.
- 65. Tale LXVII. How the Sire de Robertval, granting a traitor his life at the prayers of the man’s wife, set them both down on a desert island, and how, after the husband’s death, the wife was rescued and brought back to La Rochelle. Tale LXVIII. The wife of an apothecary at Pau, hearing her husband give some powder of cantharides to a woman who was godmother with himself, secretly administered to him such a dose of the same drug that he nearly died. Tale LXIX. How the wife of one of the King’s Equerries surprised her husband muffled in the hood of their servant-maid, and bolting meal in her stead. Tale LXX. Of the love of a Duchess of Burgundy for a gentleman who rejects her advances, for which reason she accuses him to the Duke her husband, and the latter does not believe his oaths till assured by him that he loves the Lady du Vergier. Then the Duchess, having drawn knowledge of this amour from her husband, addresses to the Lady du Vergier in public, an allusion that causes the death of both lovers; and the Duke, in despair at his own lack of discretion, stabs the Duchess himself. EIGHTH DAY. Prologue Tale LXXI. The wife of a saddler of Amboise is saved on her deathbed through a fit of anger at seeing her husband fondle a servant-maid. Tale LXXII. Kindness of the Duchess of Alençon to a poor nun whom she meets at Lyons, on her way to Rome, there to confess to the Pope how a monk had wronged her, and to obtain his Holiness’s pardon.
- 66. THE TALES OF THE HEPTAMERON OF MARGARET, QUEEN OF NAVARRE IN FIVE VOLUMES DETAILED CONTENTS OF THE TALES OF ALL FIVE VOLUMES Tale I. The pitiful history of a Proctor of Alençon, named St. Aignan, and of his wife, who caused her husband to assassinate her lover, the son of the Lieutenant-General Tale II. The fate of the wife of a muleteer of Amboise, who suffered herself to be killed by her servant rather than sacrifice her chastity Tale III. The revenge taken by the Queen of Naples, wife to King Alfonso, for her husband’s infidelity with a gentleman’s wife Tale IV. The ill success of a Flemish gentleman who was unable to obtain,
- 67. either by persuasion or force, the love of a great Princess Tale V. How a boatwoman of Coulon, near Nyort, contrived to escape from the vicious designs of two Grey Friars Tale VI. How the wife of an old valet of the Duke of Alençon’s succeeded in saving her lover from her husband, who was blind of one eye Tale VII. The craft of a Parisian merchant, who saved the reputation of the daughter by offering violence to the mother FIRST DAY—Continued. Tale VIII. The misadventure of Bornet, who, planning with a friend of his that both should lie with a serving-woman, discovers too late that they have had to do with his own wife. Tale IX. The evil fortune of a gentleman of Dauphiné, who dies of despair because he cannot marry a damsel nobler and richer than himself. Tale X. The Spanish story of Florida, who, after withstanding the love of a gentleman named Amadour for many years, eventually becomes a nun. SECOND DAY. Prologue Tale XI. (A). Mishap of the Lady de Roncex in the Grey Friars’ Convent at Thouars.
- 68. Tale XI. (B). Facetious discourse of a Friar of Touraine. Tale XII. Story of Alexander de’ Medici, Duke of Florence, whom his cousin, Lorenzino de’ Medici, slew in order to save his sister’s honour. Tale XIII. Praiseworthy artifice of a lady to whom a sea Captain sent a letter and diamond ring, and who, by forwarding them to the Captain’s wife as though they had been intended for her, united husband and wife once more in all affection. Tale XIV. The Lord of Bonnivet, after furthering the love entertained by an Italian gentleman for a lady of Milan, finds means to take the other’s place and so supplant him with the lady who had formerly rejected himself. Tale XV. The troubles and evil fortune of a virtuous lady who, after being long neglected by her husband, becomes the object of his jealousy. Tale XVI. Story of a Milanese Countess, who, after long rejecting the love of a French gentleman, rewards him at last for his faithfulness, but not until she has put his courage to the proof. Tale XVII. The noble manner in which King Francis the First shows Count William of Furstemberg that he knows of the plans laid by him against his life, and so compels him to do justice upon himself and to leave France. Tale XVIII. A young gentleman scholar at last wins a lady’s love, after enduring successfully two trials that she had made of him. Appendix to Vol. II
- 69. SECOND DAY—Continued. Tale XIX. The honourable love of a gentleman, who, when his sweetheart is forbidden to speak with him, in despair becomes a monk of the Observance, while the lady, following in his footsteps, becomes a nun of St. Clara Tale XX. How the Lord of Riant is cured of his love fora beautiful widow through surprising her in the arms of a groom THIRD DAY. Prologue Tale XXI. The affecting history of Rolandine, who, debarred from marriage by her father’s greed, betrothes herself to a gentleman to whom, despite his faithlessness, she keeps her plighted word, and does not marry until after his death Tale XXII. How Sister Marie Heroet virtuously escapes the attempts of the Prior of St. Martin in-the-Fields Tale XXIII. The undeserved confidence which a gentleman of Perigord places in the monks of the Order of St. Francis, causes the death of himself, his wife and their little child Tale XXIV. Concerning the unavailing love borne to the Queen of Castile by a gentleman named Elisor, who in the end becomes a hermit Tale XXV. How a young Prince found means to conceal his intrigue with
- 70. the wife of a lawyer of Paris Tale XXVI. How the counsels of a discreet lady happily withdrew the young Lord of Avannes from the perils of his foolish love for a lady of Pampeluna Tale XXVII. How the wife of a man who was valet to a Princess rid herself of the solicitations of one who was among the same Princess’s servants, and at the same time her husband’s guest Tale XXVIII. How a Gascon merchant, named Bernard du Ha, while sojourning at Paris, deceived a Secretary to the Queen of Navarre who had thought to obtain a pasty from him Tale XXIX. How the Priest of Carrelles, in Maine, when surprised with the wife of an old husbandman, gets out of the difficulty by pretending to return him a winnowing fan Tale XXX. How a gentleman marries his own daughter and sister unawares FOURTH DAY. Prologue Tale XXXI. Punishment of the wickedness of a Friar who sought to lie with a gentleman’s wife. Tale XXXII. How an ambassador of Charles VIII., moved by the repentance of a German lady, whom her husband compelled to drink out of her lover’s skull, reconciled husband and wife together.
- 71. Tale XXXIII. The hypocrisy of a priest who, under the cloak of sanctity, had lain with his own sister, is discovered and punished by the wisdom of the Count of Angoulême. Tale XXXIV. The terror of two Friars who believed that a butcher intended to murder them, whereas the poor man was only speaking of his Pigs. Tale XXXV. How a husband’s prudence saves his wife from the risks she incurred while thinking to yield to merely a spiritual love. Tale XXXVI. The story of the President of Grenoble, who saves the honour of his house by poisoning his wife with a salad. Tale XXXVII. How the Lady of Loué regained her husband’s affection. Tale XXXVIII. The kindness of a townswoman of Tours to a poor farm-woman who is mistress to her husband, makes the latter so ashamed of his faithlessness that he returns to his wife. Tale XXXIX. How the Lord of Grignaulx rid one of his houses of a pretended ghost. Tale XL. The unhappy history of the Count de Jossebelin’s sister, who shut herself up in a hermitage because her brother caused her husband to be slain. FIFTH DAY. Prologue
- 72. Tale XLI. Just punishment of a Grey Friar for the unwonted penance that he would have laid upon a maiden. Tale XLII. The virtuous resistance made by a young woman of Touraine causes a young Prince that is in love with her, to change his desire to respect, and to bestow her honourably in marriage. Tale XLIII. How a little chalk-mark revealed the hypocrisy of a lady called Jambicque, who was wont to hide the pleasures she indulged in, beneath the semblance of austerity. Tale XLIV. (A). Through telling the truth, a Grey Friar receives as alms from the Lord of Sedan two pigs instead of one. Tale XLIV. (B). Honourable conduct of a young citizen of Paris, who, after suddenly enjoying his sweetheart, at last happily marries. Tale XLV. Cleverness of an upholsterer of Touraine, who, to hide that he has given the Innocents to his serving-maid, contrives to give them afterwards to his wife. Tale XLVI. (A). Wicked acts of a Grey Friar of Angoulême called De Vale, who fails in his purpose with the wife of the Judge of the Exempts, but to whom a mother in blind confidence foolishly abandons her daughter. Tale XLVI. (B). Sermons of the Grey Friar De Vallès, at first against and afterwards on behalf of husbands that beat their wives. Tale XLVII. The undeserved jealousy of a gentleman of Le Perche towards
- 73. another gentleman, his friend, leads the latter to deceive him. Tale XLVIII. Wicked act of a Grey Friar of Perigord, who, while a husband was dancing at his wedding, went and took his place with the bride. Tale XLIX. Story of a foreign Countess, who, not content with having King Charles as her lover, added to him three lords, to wit, Astillon, Durassier and Valnebon. Tale L. Melancholy fortune of Messire John Peter, a gentleman of Cremona, who dies just when he is winning the affection of the lady he loves. Appendix to Vol. IV. SIXTH DAY. Prologue Tale LI. Cruelty of the Duke of Urbino, who, contrary to the promise he had given to the Duchess, hanged a poor lady that had consented to convey letters to his son’s sweetheart, the sister of the Abbot of Farse. Tale LII. Merry trick played by the varlet of an apothecary at Alençon on the Lord de la Tirelière and the lawyer Anthony Bacheré, who, thinking to breakfast at his expense, find that they have stolen from him something very different to a loaf of sugar. Tale LIII. Story of the Lady of Neufchâtel, a widow at the Court of Francis I., who, through not admitting that she has plighted her troth to the Lord des Cheriots, plays him an evil trick through the means of the Prince of Belhoste. Tale LIV. Merry adventure of a serving-woman and a gentleman named Thogas, whereof his wife has no suspicion.
- 74. Tale LV. The widow of a merchant of Saragossa, not wishing to lose the value of a horse, the price of which her husband had ordered to be given to the poor, devises the plan of selling the horse for one ducat only, adding, however, to the bargain a cat at ninety- nine. Tale LVI. Notable deception practised by an old Grey Friar of Padua, who, being charged by a widow to find a husband for her daughter, did, for the sake of getting the dowry, cause her to marry a young Grey Friar, his comrade, whose condition, however, was before long discovered. Tale LVII. Singular behaviour of an English lord, who is content merely to keep and wear upon his doublet the glove of a lady whom he loves. Tale LVIII. A lady at the Court of Francis I., wishing to prove that she has no commerce with a certain gentleman who loves her, gives him a pretended tryst and causes him to pass for a thief. Tale LIX. Story of the same lady, who, learning that her husband is in love with her waiting-woman, contrives to surprise him and impose her own terms upon him. Tale LX. A man of Paris, thinking his wife to be well and duly deceased, marries again, but at the end of fifteen years is forced to take his first wife back, although she has been living meantime with one of the chanters of Louis XII. SEVENTH DAY. Prologue Tale LXI. Great kindness of a husband, who consents to take back his wife twice over, spite of her wanton love for a Canon of Autun. Tale LXII. How a lady, while telling a story as of another, let her tongue trip in such a way as to show that what she related had happened to herself.
- 75. Tale LXIII. How the honourable behaviour of a young lord, who feigns sickness in order to be faithful to his wife, spoils a party in which he was to have made one with the King, and in this way saves the honour of three maidens of Paris. Tale LXIV. Story of a gentleman of Valencia in Spain, whom a lady drove to such despair that he became a monk, and whom afterwards she strove in vain to win back to herself. Tale LXV. Merry mistake of a worthy woman, who in the church of St. John of Lyons mistakes a sleeping soldier for one of the statues on a tomb, and sets a lighted candle on his forehead. Tale LXVI. How an old serving-woman, thinking to surprise a Prothonotary with a lady, finds herself insulting Anthony de Bourbon and his wife Jane d’Albret. Tale LXVII. How the Sire de Robertval, granting a traitor his life at the prayers of the man’s wife, set them both down on a desert island, and how, after the husband’s death, the wife was rescued and brought back to La Rochelle. Tale LXVIII. The wife of an apothecary at Pau, hearing her husband give some powder of cantharides to a woman who was godmother with himself, secretly administered to him such a dose of the same drug that he nearly died. Tale LXIX. How the wife of one of the King’s Equerries surprised her husband muffled in the hood of their servant-maid, and bolting meal in her stead. Tale LXX. Of the love of a Duchess of Burgundy for a gentleman who rejects her advances, for which reason she accuses him to the Duke her husband, and the latter does not believe his oaths till assured by him that he loves the Lady du Vergier. Then the Duchess, having drawn knowledge of this amour from her husband, addresses to the Lady du Vergier in public, an allusion that causes the death of both lovers; and the Duke, in despair at his own lack of discretion, stabs the Duchess himself. EIGHTH DAY.
- 76. Prologue Tale LXXI. The wife of a saddler of Amboise is saved on her deathbed through a fit of anger at seeing her husband fondle a servant-maid. Tale LXXII. Kindness of the Duchess of Alençon to a poor nun whom she meets at Lyons, on her way to Rome, there to confess to the Pope how a monk had wronged her, and to obtain his Holiness’s pardon.
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