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Closing remarks
Prof. Gilles Louppe
g.louppe@uliege.be
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By the end of this course, you will have a strong and comprehensive understanding of deep learning.
You will learn how to design deep neural networks for various advanced probabilistic inference tasks and how to train them.
The models covered in this course have broad applications in artificial intelligence, engineering, and science.
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The field of deep learning is evolving rapidly. What you have learned in this course is just the beginning!
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- 1 question on the fundamentals of deep learning (lectures 1 to 4)
- 1 question on advanced topics (lectures 5 to 12)
- short technical questions on your project
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- INFO8006: Introduction to Artificial Intelligence
- ELEN0062: Introduction to Machine Learning
- INFO8010: Deep Learning .italic[<-- You are here]
- INFO8004: Advanced Machine Learning
- INFO8003: Optimal decision making for complex problems
- INFO0948: Introduction to Intelligent Robotics
- ELEN0016: Computer vision
- INFOXXXX: Machine Learning Systems Design (new in 2023-2024)
- SPAT0263: Machine Learning in Space Sciences (new in 2023-2024)
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Feel free to contact us about
- MSc thesis opportunities
- PhD thesis opportunities
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- Deep Learning is more than feedforward networks.
- It is a .bold[methodology]:
- assemble networks of parameterized functional blocks
- train them from data using some form of gradient-based optimisation.
- Bricks are simple, but their nested composition can be arbitrarily complicated.
- Think like an architect: make cathedrals!
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Artificial intelligence and deep learning are transforming science, engineering and society.
As future engineers or scientists, your new skills offers you opportunities to address some of the world's biggest and important challenges. Seize them!
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That's all folks!