ECSE 552 — Deep Learning
Overview of mathematical background and basics of machine learning, deep feedforward networks, regularization for deep learning, optimization for training deep learning models, convolutional neural networks, recurrent and recursive neural networks, practical considerations,applications of deep learning, recent models and architectures in deep learning.
- Rating: 5.00 out of 5 from 1 student reviews
- Difficulty: 3.00 out of 5
- Credits: 4
- Faculty: Faculty of Engineering
- Department: Electrical & Computer Engr
- Taught by: Amin Emad
- Prerequisite: ECSE 551 or COMP 551
Sections offered
- Section 004 (Lab), Fri 10:35-11:25 am — 1 seats open
- Section 005 (Lab), Thu 1:35-2:25 pm — 9 seats open
- Section 001 (Lec), Mon Wed 4:05-5:25 pm — 0 seats open
- Section 002 (Lec), Mon Wed 4:05-5:25 pm — 7 seats open
- Section 003 (Lec), Mon Wed 4:05-5:25 pm — 3 seats open