- Mastering .NET Machine Learning
- Jamie Dixon
- 288字
- 2021-07-09 20:16:36
About the Reviewers
Reed Copsey, Jr. is the executive director of the F# Software Foundation and the CTO and co-owner of C Tech Development Corporation, a software company focused on applications and tooling for the Earth Sciences. After attending the University of Chicago, he went on to consult and work in many industries, including medical imaging, geographical information systems, analysis of retail market data, and more. He has been involved with technical and business support for numerous nonprofit organizations, and most recently enjoys spending his free time involved with the software community.
He is the organizer of the Bellingham Software Developers Network, has been a Microsoft MVP in .NET since 2010, is an avid StackOverflow contributor, and regularly speaks on F# and .NET at various user groups and conferences.
César Roberto de Souza is the author of the Accord.NET Framework and an experienced software developer. During his early university years in Brazil, he decided to create the Accord.NET Framework, a framework for machine learning, image processing, and scientific computing for .NET. Targeted at both professionals and hobbyists, the project has been used by large and small companies, big corporations, start-ups, universities, and in an extensive number of scientific publications. After finishing his MSc in the Federal University of S?o Carlos, the success of the project eventually granted him an opportunity to work and live in Europe, from where he continues its development and interacts with the growing community of users that now helps advance the project even further.
He is a technology enthusiast, with keen interest in machine learning, computer vision, and image processing, and regularly writes articles on those topics for the CodeProject, where he has won its article writing competition multiple times.
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