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  • What You Are Too Afraid to Ask About Artificial Intelligence (Part I): Machine Learning, R. (1995). “Particle Swarm Optimization”. Proceedings of IEEE International Conference on Neural Networks: 1942–1948. Koza, J.R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press. McCulloch, W. S., Pitts, W. (1943). “A Logical Calculus of the...

  • Software Suites/Platforms for Analytics, Data Mining, Data Science, and Machine Learning

    ...ore data, explore it using SQL, then train machine learning models and expose them as APIs. OpenNN, a comprehensive C++ library for neural networks research and development. Orange, open source data analytics and mining through visual programming or Python scripting. Components for visualization,...

  • KDnuggets™ News 13:n03, Feb 13

    ...eb 4, 2013. Crum & Forster, an insurance products & services company, is building a start up Data Analytics team that will lead and conduct research & development initiatives. Lead Analytic Scientist, Pharma at Verisk Health, Salt Lake City area, UT - Feb 4, 2013. Join a dynamic,...

  • KDnuggets™ News 14:n05, Mar 5

    ...The Chartis Group: Data Manager - Feb 27, 2014. Lead the team providing high quality data and analytic support for client engagements and internal research and development initiatives within the firm. Travelers: Associate, Research and Modeling - Feb 26, 2014. Travelers, a leading insurance...

  • Top 20 Python Machine Learning Open Source Projects, updated">Gold BlogTop 20 Python Machine Learning Open Source Projects, updated

    …Scikit-learn Tensorflow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research

  • Predictions for Deep Learning in 2017

    ...n this recent KDnuggets blog, deep learning professionals will also need to wrap their heads around sophisticated new approaches ranging from genetic programming and particle swarm optimization to agent-based computational economics and evolutionary algorithms. Data scientists will need to stay on...

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