Research Directions in Deep Learning

 Todays presentation is the Research Directions lecture in the Full Stack Deep Learning course (spring 2021), and is presented by Pieter Abbeel.  The lecture has a lot of focus on robotics, probably because of Pieter's background and research interests.

Hot topics such as unsupervised learning, reinforcement learning, meta learning, one-shot learning, data augmentation techniques, and new uses in science and engineering are covered.  Other hot topics such as generative models, relationships to biological models, and reference frames (things like capsule networks) are not covered, so like i said the topics covered tend to match the lecture presenters interests.

We will be following up today's presentation with another one tomorrow that takes a higher level approach to laying out current and potential future research directions by Turing award winner Yoshua Bengio.


1:  The domain randomization section is particularly interesting.  Once again pointing out the high effectiveness of data augmentation as a very practical (essential) component of modern deep learning data modeling and training.


Popular posts from this blog

Simulating the Universe with Machine Learning

CycleGAN: a GAN architecture for learning unpaired image to image transformations

Pix2Pix: a GAN architecture for image to image transformation