Deep Learning: Theory and Practice

E E 526X XW


Course Description

Review of basic theoretic tools such as linear algebra and probability. Machine learning basics will then be introduced to motivate deep learning networks. Different deep learning network architectures will be studied in detail, including their training and implementations. Applications and research problems will also be surveyed at the end of the class.

3 credits

Questions

Instructor Contact

Zhengdao Wang

Registration Information

Course Dept
E E
Course Number
526X
Section
XW
Credit Hours
3
Semester
Fall 2019
Dates
Aug 26 - Dec 20
Prerequisites
MATH 207, E E 322
Delivery Fee
$570.00 *
* Delivery Fee is additional to Tuition & Fees.

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