Objective:
Student-focused study of the main deep learning methods and their
applications.
Syllabus:
Basic models: Convolutional Neural Networks (CNN), stacked/denoising autoencoders,
Recurrent Neural Networks (including Long-Short-Term Memory Networks - LSTM),
Generative Adversarial Networks (GAN).
Advanced models. Study of development frameworks for deep learning. Applications
of deep learning models for real-world problems.
Recommended literature:
see here
Duration/credits:
