Jeffrey Heaton

Jeffrey Heaton

Greater St. Louis
7K followers 500+ connections

About

Jeff Heaton, Ph.D., is a technologist, AI researcher, and social media influencer…

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Contributions

Activity

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Education

Licenses & Certifications

Volunteer Experience

  • Saint Louis University Graphic

    Advisory Board Member

    Saint Louis University

    - Present 5 years 6 months

    Science and Technology

    I server as an advisory board member for the Saint Louis University Department of Computer Science. https://cs.slu.edu/about/advisory-board

  • Washington University in St. Louis - Olin Business School Graphic

    Center for Analytics and Business Insights Board Member

    Washington University in St. Louis - Olin Business School

    - Present 3 years 3 months

    Education

  • IEEE Graphic

    Peer Reviewer for SoutheastCON

    IEEE

    - 5 years

    Science and Technology

    I acted as a peer reviewer for several papers in the areas of Machine Learning and Health Informatics for the IEEE SoutheasternCon conference since 2015.

  • Junior Achievement USA Graphic

    Volunteer

    Junior Achievement USA

    - 3 years

    Education

    Presenter for RGA at the JA career fair for middle and high school students.

Publications

  • Predictive Modeling with Bayesian Networks

    Forecasting & Futurism newsletter, Society of Actuaries

    A overview of the Bayesian Network with a brief example applied to the FOREX market.

  • Introduction to the Math of Neural Networks

    This book introduces the reader to the basic math used for neural network calculation. This book assumes the reader has only knowledge of college algebra and computer programming. This book begins by showing how to calculate output of a neural network and moves on to more advanced training methods such as backpropagation, resilient propagation and Levenberg Marquardt optimization. The mathematics needed by these techniques is also introduced. Mathematical topics covered by this book include…

    This book introduces the reader to the basic math used for neural network calculation. This book assumes the reader has only knowledge of college algebra and computer programming. This book begins by showing how to calculate output of a neural network and moves on to more advanced training methods such as backpropagation, resilient propagation and Levenberg Marquardt optimization. The mathematics needed by these techniques is also introduced. Mathematical topics covered by this book include first, second, Hessian matrices, gradient descent and partial derivatives. All mathematical notation introduced is explained. Neural networks covered include the feedforward neural network and the self organizing map. This book provides an ideal supplement to our other neural books. This book is ideal for the reader, without a formal mathematical background, that seeks a more mathematical description of neural networks.

    See publication
  • Basic Market Forecasting with Encog Neural Networks

    DevX

    Article describing the use of Encog for financial prediction.

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  • Programming Neural Networks with Encog3 in C#

    Heaton Research, Inc.

    Encog is an advanced Machine Learning Framework for Java, C# and Silverlight. This book focuses on using the neural network capabilities of Encog with the C# programming language. This book begins with an introduction to the kinds of tasks neural networks are suited towards. The reader is shown how to use classification, regression and clustering to gain new insights into data. Neural network architectures such as feedforward, self organizing maps, NEAT, and recurrent neural networks are…

    Encog is an advanced Machine Learning Framework for Java, C# and Silverlight. This book focuses on using the neural network capabilities of Encog with the C# programming language. This book begins with an introduction to the kinds of tasks neural networks are suited towards. The reader is shown how to use classification, regression and clustering to gain new insights into data. Neural network architectures such as feedforward, self organizing maps, NEAT, and recurrent neural networks are introduced. This book also covers advanced neural network training techniques such as back propagation, quick propagation, resilient propagation, Levenberg Marquardt, genetic training and simulated annealing. Real world problems such as financial prediction, classification and image processing are introduced.

    See publication
  • Programming Neural Networks with Encog3 in Java

    Heaton Research, Inc.

    Encog is an advanced Machine Learning Framework for Java, C# and Silverlight. This book focuses on using the neural network capabilities of Encog with the Java programming language. This book begins with an introduction to the kinds of tasks neural networks are suited towards. The reader is shown how to use classification, regression and clustering to gain new insights into data. Neural network architectures such as feedforward, self organizing maps, NEAT, and recurrent neural networks are…

    Encog is an advanced Machine Learning Framework for Java, C# and Silverlight. This book focuses on using the neural network capabilities of Encog with the Java programming language. This book begins with an introduction to the kinds of tasks neural networks are suited towards. The reader is shown how to use classification, regression and clustering to gain new insights into data. Neural network architectures such as feedforward, self organizing maps, NEAT, and recurrent neural networks are introduced. This book also covers advanced neural network training techniques such as back propagation, quick propagation, resilient propagation, Levenberg Marquardt, genetic training and simulated annealing. Real world problems such as financial prediction, classifiction and image processing are introduced.

    See publication
  • Interfacing to AIM with Java

    Java Developer's Journal

    Article describing how to send messages to AIM via Java.

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  • Programming Neural Networks in Java

    Java Developer's Journal

    Article about neural networks in Java.

    See publication
  • Programming Spiders, Bots, and Aggregators in Java

    Sybex

    The content and services available on the web continue to be accessed mostly through direct human control. But this is changing. Increasingly, users rely on automated agents that save them time and effort by programmatically retrieving content, performing complex interactions, and aggregating data from diverse sources. Programming Spiders, Bots, and Aggregators in Java teaches you how to build and deploy a wide variety of these agents-from single-purpose bots to exploratory spiders to…

    The content and services available on the web continue to be accessed mostly through direct human control. But this is changing. Increasingly, users rely on automated agents that save them time and effort by programmatically retrieving content, performing complex interactions, and aggregating data from diverse sources. Programming Spiders, Bots, and Aggregators in Java teaches you how to build and deploy a wide variety of these agents-from single-purpose bots to exploratory spiders to aggregators that present a unified view of information from multiple user accounts.

    You will quickly build on your basic knowledge of Java to quickly master the techniques that are essential to this specialized world of programming, including parsing HTML, interpreting data, working with cookies, reading and writing XML, and managing high-volume workloads. You'll also learn about the ethical issues associated with bot use--and the limitations imposed by some websites.

    This book offers two levels of instruction, both of which are focused on the library of routines provided on the companion CD. If your main concern is adding ready-made functionality to an application, you'll achieve your goals quickly thanks to step-by-step instructions and sample programs that illustrate effective implementations. If you're interested in the technologies underlying these routines, you'll find in-depth explanations of how they work and the techniques required for customization.

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  • High-Speed WIN32 Animation

    The C/C++ Users Journal

    How to use WING with WIN32 in C.

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Projects

  • Encog Machine Learning Framework

    -

    Encog is an advanced machine learning framework that supports a variety of advanced algorithms, as well as support classes to normalize and process data. Machine learning algorithms such as Support Vector Machines, Artificial Neural Networks, Genetic Programming, Bayesian Networks, Hidden Markov Models and Genetic Algorithms are supported. Most Encog training algoritms are multi-threaded and scale well to multicore hardware. Encog can also make use of a GPU to further speed processing time. A…

    Encog is an advanced machine learning framework that supports a variety of advanced algorithms, as well as support classes to normalize and process data. Machine learning algorithms such as Support Vector Machines, Artificial Neural Networks, Genetic Programming, Bayesian Networks, Hidden Markov Models and Genetic Algorithms are supported. Most Encog training algoritms are multi-threaded and scale well to multicore hardware. Encog can also make use of a GPU to further speed processing time. A GUI based workbench is also provided to help model and train machine learning algorithms. Encog has been in active development since 2008. Encog is available for Java, .Net and C/C++.

    Other creators
    • Olivier Guiglionda
    See project

Organizations

  • Upsilon Pi Epsilon (UPE)

    Member

    - Present

    Computer science honor society. http://upe.acm.org/

  • IEEE

    Senior Member

    - Present

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