Home > Articles > Business & Management

  • Print
  • + Share This
This chapter is from the book

Neural Networks and Deep Learning

Deep learning has recently received considerable attention in business media; analysts successfully used the technique in a number of highly visible data mining competitions. Deep Learning is an extension of Neural Networks; in this section, we discuss both techniques.

Neural Networks

Artificial neural networks are computational models inspired by the study of brains and the nervous system; they consist of a network of nodes (“neurons”) connected by directed graphs (“synapses”). Neuroscientists developed neural networks as a way to study learning; their methods are broadly applicable to problems in predictive analytics.

In a neural network, each neuron accepts mathematical input, processes the inputs with a transfer function, and produces mathematical output with an activation function. Neurons operate independently on their local data and on input from other neurons.

Neural networks may use a range of mathematical functions as activation functions. While a neural network may use linear functions, analysts rarely do so in practice; a neural network with linear activation functions and no hidden layer is a linear model. Analysts are much more likely to use nonlinear activation functions, such as the logistic function; if a linear function is sufficient to model the target, there is no reason to use a neural network.

The nodes of a neural network form layers, as shown in Exhibit 9.6. The input layer accepts mathematical input from outside the network, while the output layer accepts mathematical input from other neurons and transfers the results outside the network. A neural network may also have one or more hidden layers that process intermediate computations between the input layer and output layer.

Exhibit 9.6

Exhibit 9.6 Neural Network Topology

When you use neural networks for predictive analytics, the first step is to specify the network topology. The predictor variables serve as the input layer, and the output layer is the response measure. The optional hidden layers enable the model to learn arbitrarily complex functions. Analysts use some heuristics to determine the number of hidden layers and their size, but some trial and error is required to determine the best network topology.

There are many different neural network architectures, distinguished by topology, flow of information, mathematical functions, and training methods. Widely used architectures include the following:

  • Multilayer perceptron
  • Radial basis function network
  • Kohonen self-organizing network
  • Recurrent networks (including Boltzmann machines)

Multilayer perceptrons, which are widely used in predictive analytics, are feedforward networks; this means that a neuron in one layer can accept input from any neuron in a previous layer but cannot accept input from neurons in the same layer or subsequent layers. In a multilayer perceptron, the parameters of the model include the weights assigned to each connection and to the activation functions in each neuron. After the analyst has specified a neural network’s topology, the next step is to determine the values for these parameters that minimize prediction errors, a process called training the model.

Many methods are available to train a neural network; for multilayer perceptrons, the most widely used class of methods is backpropagation, which uses a data set in which values of the target (output layer) are known to infer parameter values that minimize errors. The method proceeds iteratively; first computing the target value with training data and then using information about prediction errors to adjust weights in the network.

Several different backpropagation algorithms exist; gradient descent and stochastic gradient descent are the most widely used. Gradient descent uses arbitrary starting values for the model parameters and computes an error surface; it then seeks out a point on the error surface that minimizes prediction errors. Gradient descent evaluates all cases in the training data set each time it iterates; stochastic gradient descent works with a random sample of cases from the training data set. Consequently, stochastic gradient descent converges more quickly than gradient descent but may produce a less accurate model. The gradient descent algorithms can also train other types of models, including support vector machines and logistic regression.

Alternative algorithms for training a backpropagation neural network include the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm and its limited memory variant (L-BFGS) and the conjugate gradient algorithm. These algorithms can perform significantly better at minimizing prediction errors but tend to require more computing resources.

Radial basis function (RBF) networks have one or more hidden layers representing distance measures modeled with a Gaussian function. Analysts train RBF networks with a maximum likelihood algorithm. Compared to multilayer perceptrons, RBF networks are less likely to confuse a local minimum in the error surface for the desired global minimum; however, they are also more prone to overfitting.

Kohonen self-organizing networks (self-organizing maps) are a technique for unsupervised learning with limited application in predictive analytics. Refer to the appendix for a discussion of unsupervised learning with neural networks.

In a recurrent neural network (RNN), information flows in either direction among the layers; this contrasts with feedforward networks, where information flows in one direction only: from the input layer to the hidden layers to the output layer. The most important type of RNN is the restricted Boltzmann machine, an architecture used in deep learning (discussed in the following section).

The key strength of neural networks is their ability to model very complex nonlinear functions. Neural networks are also well suited to highly dimensional problems, where the number of potential predictors is very large.

The key weakness of neural networks is their tendency toward overlearning. A network learns to minimize prediction error on the training data, which is not the same thing as minimizing prediction error in a business application. As with other modeling techniques, analysts must test models produced with neural networks on an independent sample.

Analysts using the neural network technique must make a number of choices about the network topology, transfer functions, activation functions, and the training algorithm. Because there is very little theory to guide the choices, the analyst must rely on trial and error to find the best model. Consequently, neural networks tend to consume more analyst time to produce a useful model.

Leading commercial software packages for machine learning, including IBM SPSS Modeler, RapidMiner, SAS Enterprise Miner, and Statistica, support neural networks, as do in-database libraries such as dbLytix and Oracle Data Mining. Multiple packages in open source R support neural networks; in Python, the PyBrain package offers extensive capabilities.

Deep Learning

Deep learning is a class of model training techniques based on feature learning, or the capability to learn a concise set of “features” from complex unlabeled data. In practice, a deep neural network is a neural network with multiple hidden layers trained sequentially with unsupervised learning techniques.

Interest in deep learning stems from a number of notable recent successes in machine learning competitions:

  • International Conference on Document Analysis and Recognition (2009)
  • IJCNN Traffic Sign Recognition Competition (2011, 2012)
  • ISBI Segmentation of Neuronal Structures in Electron Microscopy (2012)
  • Merck Molecular Activity Challenge (2012)

The theory of deep learning dates to the 1980s; however, practical application lagged due to the computational complexity and resources needed. The increased availability and reduced cost of GPU devices and other platforms for high-performance computing has provided analysts with the computing power to experiment with deep learning techniques.

Deep neural networks are prone to overfitting due to the introduction of additional abstraction layers; analysts manage this tendency with regularization techniques. Models must be tested and validated to ensure they generalize to fresh cases.

Commercial software for deep learning is limited at present. Neither SAS nor SPSS currently support the capability out of the box: PROC Neural in SAS Enterprise Miner 13.1 permits users to build neural networks with an unlimited number of hidden layers but lacks the ability to build Boltzmann machines, a necessary tool for deep learning. There are, however, a number of open source deep learning libraries available in C, Java, and Python as well as a MATLAB Toolbox.

  • + Share This
  • 🔖 Save To Your Account

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.

Overview


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information


To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.

Surveys

Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.

Newsletters

If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information


Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.

Security


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.

Children


This site is not directed to children under the age of 13.

Marketing


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information


If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.

Choice/Opt-out


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information


Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents


California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure


Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.

Links


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact


Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice


We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020