Lesson Description
In this course, you will learn the Advanced Topics in Neural Networks, specifically advanced topics on Deep Learning models, understand how to build advanced neural networks, and learn how to lead successful machine learning projects. This course introduces you to deep learning: the state-of-the-art approach to building artificial intelligence algorithms. Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like natural language processing, image recognition, speech recognition, robotics, etc. In this course, we cover the basic components of deep learning, what it means, how it works, and develop code necessary to build various algorithms such as deep convolutional networks, auto-encoders, generative adversarial networks, and recurrent neural networks.
Weekly Schedule
Mondays 8-10, Location: Learning Management System (LMS)
Files
| Files Name | Presentations | Home works | Quizzes | Midterm | Final Exam | Related Documents |
|---|---|---|---|---|---|---|
| Chapter 1 | Download | Download | Download | Download | Download | Download |
| Chapter 2 | Download | Download | Download | Download | Download | Download |
| Chapter 3 | Download | Download | Download | Download | Download | Download |
| Chapter 4 | Download | Download | Download | Download | Download | Download |
| Chapter 5 | Download | Download | Download | Download | Download | Download |
| Chapter 5 | Download | Download | Download | Download | Download | Download |
| Chapter 6 | Download | Download | Download | Download | Download | Download |
| Chapter 7 | Download | Download | Download | Download | Download | Download |
| Chapter 8 | Download | Download | Download | Download | Download | Download |
Course References
1. Deng, Li, and Dong Yu. "Deep Learning: Methods and Applications", 2014
2. Yu, Dong, and Li Deng. "Automatic Speech Recognition: A deep Learning Approach", 2015.
3. Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. "Deep Learning", 2016.
4. Bishop, Christopher M. "Pattern Recognition and Machine Learning", 2006.
Current Teacher Assistants
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Former Teacher Assistants
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Contact
Location:
HerzarJarib Street, Azadi Square, University of Isfahan, Isfahan, Iran
Email:
r.rasti@eng.ui.ac.ir
Call:
+98 31 3793 5638