Special Studies in

Applied Computational Intelligence

horizontal rule

Home
Bibliography
Students
Scheduling
Grades
Student projects
Datasets
Frameworks

 

Objective:

Student-focused study of the main deep learning methods and their applications.

Syllabus:

Basic models: Convlutional Neural Networks (CNN), stacked/denoising autoencoders, Recurrent Neural Networks (including Long-Short-Term Memory Networks - LSTM). Advanced models. Study of development frameworks for deep learning. Applications of deep learning models for real-world problems.

Recommended literature:

see here

Duration/credits:

bullet45 hours/3 credits (12 weeks)

Room/Time:

bulletRoom B303 (Electronics Dept. building), Fridays, ~08:20-10:00

Teachers:

bullet

Heitor S. Lopes [h s l o p e s  -->  utfpr . edu . br]

bullet André E. Lazzaretti [l a z z a r e t t i  -->  utfpr . edu . br]
 

horizontal rule

Copyright H.S.Lopes
Last update: 09 julho, 2018.