Free Courseware

A short lecture series on Autoencoder and Variational Autoencoder

Lecture notes can be downloaded here

Objective and Scope

With the rapid advancement of technology, deep learning-based architectures have emerged as a powerful tool for solving a multitude of real-world problems. These architectures, including transformers and diffusion models, have been widely embraced across different communities to address a diverse range of challenges in computer vision, image processing, speech processing, NLP, and more.

In recent years, top-tier conferences such as CVPR, ECCV, ICCV, NIPS, and ICLR have witnessed a surge in research leveraging deep learning models such as CNNs, transformers, diffusion models, GANs, and other related architectures. These models have been extensively explored and applied to tackle complex problems in various domains.

This workshop offers a practical-oriented approach, providing participants with hands-on experience in applied deep learning. Through interactive sessions and practical exercises, attendees will gain a comprehensive understanding of deep learning concepts and techniques, empowering them to apply these skills effectively in their respective fields.