Comment on Deep Learning 101parentComments−mbeissingerOP12yDefinitely a solid foundation in linear algebra and statistics (mostly Bayesian) are necessary for understanding how the algorithms work. Check out the wiki portals (http://en.wikipedia.org/wiki/Machine_learning) and (http://en.wikipedia.org/wiki/Artificial_intelligence) for overviews of the most common approaches.Also, Andrew Ng's coursera course on machine learning is amazing (https://www.coursera.org/course/ml) as well as Norvig and Thrun's Udacity course on AI (https://www.udacity.com/course/cs271)
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Definitely a solid foundation in linear algebra and statistics (mostly Bayesian) are necessary for understanding how the algorithms work. Check out the wiki portals (http://en.wikipedia.org/wiki/Machine_learning) and (http://en.wikipedia.org/wiki/Artificial_intelligence) for overviews of the most common approaches.
Also, Andrew Ng's coursera course on machine learning is amazing (https://www.coursera.org/course/ml) as well as Norvig and Thrun's Udacity course on AI (https://www.udacity.com/course/cs271)