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College of Engineering

CS 435 – Applied Deep Learning (4)

Explores and applies several neural network architectures, including fully-connected, convolutional, and sequence model based network structures. Topics including gradient descent and optimization, feed forward and back propagation methods, loss functions, activation functions, training, testing, and validation of the networks, as well as other types of advanced networks. Applications including areas such as text sentiment analysis, image classification, and language translation. This course may be subject to Enforced Prerequisites that restrict registration into the course. Check the offerings below for more information.

For more information, contact OSU Ecampus at 800-667-1465 (option 1) or ecampus.ess@oregonstate.edu.

Continue to Registration.

TermCRNSecCrP/NInstructorTypeStatusCapAvailWL CapWL Avail
F25198664004Gates, A.Online Open12212200
Registration Restrictions
Enforced Prereqs: CS 332 [C] and (CS 432 [C] or CS 434 [C] or CS 434H [C] )
Major Restrictions: +307 (Computer Science)
Campus Restrictions: +DSC (Dist. Degree Corvallis Student)
College Limitations: +16 (Engr)
Class Notes: Campus restrictions for this section end Monday of Week 10.This course requires online proctored testing, which mayinclude testing fees and the use of security measures, suchas a scan of your testing environment. Please carefullyreview online proctor test information at:https://ecampus.oregonstate.edu/services/proctoring .

Textbooks

Syllabus: Available in Canvas to students enrolled in this course. Computer Science syllabi may also be found on the College of Engineering Course and Syllabus List.
Find textbooks for CS 435 at the OSU Beaver Store (current term only). For questions related to course materials, contact the OSU Beaver Store.

Legend
= Signifies the course as a Baccalaureate Core Course.
= Signifies that fees may apply to the course.
+ = Include restriction.
- = Exclude restriction.
* = Prereq may be taken prior to or simultaneously with this course.