Deep Learning
Course Description
In this course we will learn about the basics of deep neural networks, and their applications to various tasks. The course aim is to present the mathematical, statistical, and computational challenges of building stable representations for high-dimensional data.
Course Objectives:
| · | Learn the tools required for building Deep Learning models |
| · | Explore multiple architectures and understand how to fine-tune and continuously improve models |
| · | Learn how the same task can be solved using multiple Deep Learning approaches |
Course Contents:
| · | Introduction |
| · | Shallow and Deep Neural Networks |
| · | Explaining and Harnessing Adversarial Examples |
| · | Optimization |
| · | Gradient Checking |
| · | Hyperparameter tuning |
| · | Programming Frameworks |
| · | Conditional GAN, CycleGAN |
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