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We Will Learn Theoretical and Practical Tools to Build, Design And Analyze Deep Networks, With an Emphasis On Supervised Learning. For Example, Properties and Covnergence of Gradient Desecnt and Its Variants, Efficient Differntiation, Multilayer Nets (approximation And Symmetry), Convnets (and Extentions) For Visual Tasks, Training Methods and Their Analysis, Networks For Serial Data, And Pre-training. Learning Outcomes# With The Completionof The Course, The Students# 1. Will Be Familiar With The Main Models and Common Training Methods For Deep Learning. 2. Will Be Able to Code (in Python, Using The Pytorch Framework) For Deep Neural Network, Train It, and Use It. 3. Will Be Able to Understand The Considerations Required to Tune Deep Networks For Achieving Good Perfomance, and The Relevant Theoretical Results (when Such Exist).

Faculty: Electrical and Computer Engineering
|Undergraduate Studies |Graduate Studies

Pre-required courses

46195 - Machine Learning or 236756 - Introduction to Machine Learning

Course with no extra credit

97209 - Machine Learning 2 236777 - Deep Learning and Its Applications 236781 - Deep Learning On Computation