Description

The course will be taught using a combination of lectures, readings, and hands-on exercises. The lectures will provide an overview of the key concepts in deep learning. The readings will provide more in-depth coverage of the topics. The hands-on exercises will give students the opportunity to apply the concepts they have learned to real-world data sets.

Compétences visées

By the end of the course, students will be able to:
• Understand the basic concepts of deep learning
• Build and train deep learning models
• Apply deep learning models to real-world problems
• Evaluate the performance of deep learning models
• Interpret the results of deep learning models

Bibliographie

• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
• Nielsen, M. A. (2015). Neural networks and deep learning. Determination Press.
• Murphy, K. P. (2012). Machine learning: A probabilistic perspective. MIT Press.

Contacts

Responsable(s) de l'enseignement

• Karim Tout