Explore recent advances in deep learning through this comprehensive lecture by Professor Pieter Abbeel. Delve into various research directions, including unsupervised learning, reinforcement learning, and their combinations. Discover meta reinforcement learning, few-shot imitation learning, and domain randomization techniques. Examine the applications of deep learning in science and engineering, and gain insights into overarching research trends. Learn strategies to stay updated with the rapidly evolving field of deep learning. The lecture covers topics from basic introductions to advanced concepts, providing a thorough overview of current research directions in deep learning.
Research Directions - Full Stack Deep Learning - Spring 2021
Overview
Syllabus
- Introduction
- Unsupervised Learning
- Reinforcement Learning
- Unsupervised Reinforcement Learning
- Meta Reinforcement Learning
- Few-Shot Imitation Learning
- Domain Randomization
- Deep Learning For Science and Engineering
- Overarching Research Trends
- How To Keep Up
Taught by
The Full Stack
Related Courses
-
Pieter Abbeel on Research Directions - Full Stack Deep Learning - November 2019
-
Deep Learning Crash Course for Beginners
4.0 -
Deep Learning and Reinforcement Learning
3.0 -
AI skills: Introduction to Unsupervised, Deep and Reinforcement Learning
-
AI and Machine Learning Algorithms and Techniques
-
Research Directions - Full Stack Deep Learning - March 2019
