veyepar: 1A First Course in Deep Learning

Author(s):	Jeremy Watt, Reza Borhani
Location	Daley 512
Date	may Fri 06	Days Raw Files
Start	14:00	First Raw Start	error-in-template
Duration	210:00	Offset	None
End	17:30	Last Raw End
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http://mdp.cdm.depaul.edu/DePy2016/default/schedule
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Abstract: Due to their wide applicability, the tools of Deep Learning have quickly become some of the most important for today’s researchers in computer vision, machine learning, robotics, and related fields. In particular, over the past several years the tools of Deep Learning have been used to great effect in both academia and industry, producing state of the art results on a variety of challenging computer vision and speech recognition problems1,2,3,4,5. However understanding the foundations of deep learning can be intimidating to the uninitiated, as can comprehending the details of their implementation which demands an understanding of numerical optimization often unfamiliar to those with a traditional computer science or engineering background. In this tutorial we provide a user-friendly introduction to the basic tools of Deep Learning, describe their many applications, discuss how they relate to more traditional ideas in machine learning, and provide an introduction to most useful techniques from numerical optimization crucial to their implementation. To make full use of this tutorial one only needs a basic understanding of linear algebra and vector calculus. No prior knowledge of numerical optimization or machine learning is expected. Additionally, we intend to provide Python code for all demos presented in this talk.

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