Announcements
- 3rd Assignment is released! Check the assignments page.
- Resources for TA Sessions are available in schedule page.
- Main Session slides are now available in schedule page.
About This Course
The course is a combination of applied and theoretical aspects of deep learning. In addition to discussing recent state-of-the-art models in both supervised and unsupervised learning, we delve into the details of various network architectures for processing texts and images. Special emphasis will be put on learning how to work with deep learning libraries, particularly Tensorflow 2.0.
In this course you will learn the foundations of deep learning, understand various neural network architectures, and practice developing machine learning projects from scratch.
Learning Objectives
The course is a combination of applied and theoretical aspects of deep learning. In addition to discussing recent state-of-the-art models in both supervised and unsupervised learning, we delve into the details of various network architectures for processing texts and images. Special emphasis will be put on learning how to work with deep learning libraries, particularly Tensorflow 2.0.
Course Weekly Plan
Flipped style classroom:
- Lecture (Sundays)
- TA Session (Tuesdays)
- Project Mentorship (Wednesdays)
Course instructor
Mohammad Taher Pilehvar
Teaching Assistants
Kiamehr Razaee
Mohsen Tabasi
Mentors
Amir Mohammad Salehoof
Pouria Khani