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DSCI 471 Applied Deep Learning 3.0 Credits

The goals of this course are to introduce basic theory of deep learning in data science applications, to understand how deep learning algorithms work at a high level, and to apply deep learning algorithms to key data science problems in different disciplines. The course introduces all relevant topics in deep learning: neural networks, backpropagation, convolution neural networks, recurrent neural networks and deep reinforcement learning. Students will be exposed to various representative algorithms in the concept level and learn their trade-offs. Students will gain hands-on experiences with assignments and a term project. Students will be prepared to attack new problems using various deep learning methods.

College/Department: College of Computing and Informatics
Repeat Status: Not repeatable for credit
Prerequisites: INFO 213 [Min Grade: D] and MATH 201 [Min Grade: D]

Data Science

http://catalog.drexel.edu/undergraduate/collegeofcomputingandinformatics/datascience/

...Credits Summer Credits COOP EXPERIENCE COOP EXPERIENCE DSCI 471 3.0 INFO 442 3.0...

Economics and Data Science

http://catalog.drexel.edu/undergraduate/schoolofeconomics/economicsanddatascience/

...Credits COOP EXPERIENCE COOP EXPERIENCE DSCI 351 3.0 DSCI 471 3.0 INFO 323...

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