General Information
Objective: Study of the main deep learning methods and their applications.
Syllabus: Machine learning basics; Deep Feedforward Networks; Convolutional Neural Networks (CNN), Recurrent Neural Networks; Segmentation and Object Recognition; Frameworks for deep learning and Practical Aspects; Applications of deep learning models for real-world problems.
Duration: 60 hours (17 weeks).
Time: Tuesdays (18:40 - 21:10) - synchronous (SYN) and asynchronous (ASYN) classes.
Grade: Assignments (50%) and Final Project (50%).
Lecturer: André Eugenio Lazzaretti and Heitor Silvério Lopes.
Collaborators (PhD students): Andrei Inácio, Matheus Gutoski, Anderson Brilhador, and Clayton Kossoski.
Bibliography and Support Materials
Books:
Goodfellow, I., Bengio, Y., Courville, A. Deep Learning. MIT Press, 2016.
Chollet, F., Deep Learning with Python. Manning, 2018
Raschka, S. Python Machine Learning. Packt, 2020
Other Courses:
Andrew Ng at Coursera.
Sebastian Raschka at UW Madison.
Fei Fei Li et al. at Stanford (most similar).
Introduction to Deep Learning at MIT.
Week 1 - 16/08
Content:
(SYN) General overview and Rules.
Week 4 - 06/09
Assignment - Regression - cont.
Week 7 to 17 - 27/09 to 20/12 - in sync with Deep Learning Course - CPGEI
Check here.