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Articles by JeffHeaton (Articles: 5)

Articles: 5

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Average article rating: 4.85

Algorithms & Recipes
Evolutional
Posted: 22 Feb 2014   Updated: 10 Mar 2014   Views: 43,420   Rating: 4.95/5    Votes: 16   Popularity: 5.95
Licence: The Apache License, Version 2.0      Bookmarked: 37   Downloaded: 27
Use a Human Based Genetic Algorithm (HBGA) to create new and interesting cellular automata similar to Conway's Game of Life.
General
Posted: 9 Jun 2010   Updated: 9 Jun 2010   Views: 120,970   Rating: 4.80/5    Votes: 8   Popularity: 4.39
Licence: The GNU Lesser General Public License (LGPLv3)      Bookmarked: 22   Downloaded: 2,458
Learn the basics of how to install and use OpenCL with Java, unleash the power of your GPU.
Neural Networks
Posted: 17 Jan 2010   Updated: 17 Jan 2010   Views: 51,580   Rating: 4.69/5    Votes: 9   Popularity: 4.45
Licence: The GNU Lesser General Public License (LGPLv3)      Bookmarked: 23   Downloaded: 1,999
An introduction to creating neural networks with the Encog Framework for Java.
Artificial Intelligence and Machine Learning
Neural Networks
Posted: 26 Jan 2010   Updated: 26 Jan 2010   Views: 51,490   Rating: 4.95/5    Votes: 29   Popularity: 7.23
Licence: The Code Project Open License (CPOL)      Bookmarked: 60   Downloaded: 3,563
An introduction to creating neural networks with the Encog Framework for C#.
Posted: 16 Oct 2012   Updated: 16 Oct 2012   Views: 63,150   Rating: 4.86/5    Votes: 6   Popularity: 3.76
Licence: The Apache License, Version 2.0      Bookmarked: 27   Downloaded: 1,215
Use Encog genetic algorithms, simulated annealing, neural networks and more with HTML5 Javascript.
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JeffHeaton
Publisher
United States United States
Jeff Heaton, Ph.D., is a data scientist, an adjunct instructor for the Sever Institute at Washington University, and the author of several books about artificial intelligence. Jeff holds a Master of Information Management (MIM) from Washington University and a PhD in computer science from Nova Southeastern University. Over twenty years of experience in all aspects of software development allows Jeff to bridge the gap between complex data science problems and proven software development. Working primarily with the Python, R, Java/C#, and JavaScript programming languages he leverages frameworks such as TensorFlow, Scikit-Learn, Numpy, and Theano to implement deep learning, random forests, gradient boosting machines, support vector machines, T-SNE, and generalized linear models (GLM). Jeff holds numerous certifications and credentials, such as the Johns Hopkins Data Science certification, Fellow of the Life Management Institute (FLMI), ACM Upsilon Pi Epsilon (UPE), a senior membership with IEEE. He has published his research through peer reviewed papers with the Journal of Machine Learning Research and IEEE.


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