Introduction

Introduction #

Main topics #

  • Easy-to-understanding Introduction to and advances in some regions of deep learning (Self-supervised learning, meta learning, transfer learning, etc.) - determined by everyone in this seminar.
  • Some practical topics like backbones, optimizers and data augmentations may also incorporated depend on progress.

You can get #

  • Knowledge about fundamental and advanced methodologies in machine learning.

  • A proof of your study experience: this website - Every contribution you make to this seminar will be recorded

    Hope everyone to deeply involved in this seminar, you can

    • Serve as a keynote speaker
    • Contribute some blogs and publish on the website

  • Group Study

    • Work together to sovle for some problems - research experience

    • High-efficent learning pace & Study atmosphere

More #

  • There are no explicit grades in this seminar, the only and foremost objective is to learn together.
  • If you are audience, free free to come or not. (update the topics and main contents in advance).
  • If you are keynote speaker, there are requirements for your representation (discuss later)