Deep Learning is the most highly sought-after skill in Artificial Intelligence. In this module, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, Recurrent Neural Networks, Transformers, Regularization, Adversarial Attacks, Deep Reinforcement Learning, Interpretability, as well as bleeding-edge Stochastic foundational regards.
This course aims at students being able to:
a) To describe and classify the fundamental design principles of deep learning systems.
b) To describe and use widely accepted techniques for deep network implementation.
c) To evaluate research articles on Deep Learning.
d) To be capable of carrying out a full-cycle Deep Learning Project
Course Content:
• Introduction to deep learning.
• Neural Network Basics.
• The foundational principle of Learning Representations.
• Attacking neural networks with Adversarial Examples
• Adversarial Robustness.
• Hyperparameter Tuning, Batch Normalization.
• Stochastic Gradient Descent Training.
• Convolutional Layers.
• Recurrent Layers.
• Self-Attention Layers.
• Generative networks: Variational autoencoders; generative adversarial networks.
• Interpretability of Neural Networks.
• Deep Reinforcement Learning.
• Meta-learning.
