#191 overall
CS330 Deep Multi-Task and Meta Learning
Stanford University
3 recommenders · 4 threads · recommended in 2 years, 2020–2022 · score 2.3
CS330 is a Stanford graduate course on deep multi-task learning and meta-learning, taught by Chelsea Finn, covering how models learn to adapt quickly across many tasks. Its lectures are published on YouTube and its course site hosts the materials.
Commenters describe it as a one-stop resource for multi-task and meta-learning and list it among Stanford's excellent free AI course notes and lectures.
Machine Learning & AIadvancedfreevideo lectures#meta-learning#multi-task learning#deep learning#finn
Recommendations by year
20062026
Stanford grad courses are excellent. Just following the exercises will get you very far:
* AI Foundations: https://stanford-cs221.github.io/autumn2022/
* Meta Learning: https://cs330.stanford.edu/
* Theoretical ML: https://cs229.stanford.edu/
* NLP: https://web.stanford.edu/class/cs224n/
* Computer vision: http://cs231n.stanford.edu/
* Reinforcement Learning: https://web.stanford.edu/class/cs234/
Read Blogs:
* DeepMind: https://www.deepmind.com/blog
* Google Research: https://ai.googleblog.com/
Replicate papers that you are interested in with a twist specific to your use cases
* RL Spinning UP:https://spinningup.openai.com/en/latest/
Highly recommend the Stanford course notes on AI. I've found these useful:
* CS224n natural language processing
* CS330 Meta Learning
* CS224U Natural language understanding
* CS234 Reinforcement Learning
* CS221 Artificial intelligence
* CS229 theoretical machine learning
The course notes can be found at CS(x).Stanford.edu such as
cs229.stanford.edu
Of course the main problem isn't that there's not enough good material. The problem is that there's too much! So a course that teaches how to pick the most important courses for oneself is sorely needed
I would recommend auditing Stanford courses in following order:
1. CS231n Machine Vision https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1T...
2. CS234 Reinforcement Learning https://www.youtube.com/watch?v=FgzM3zpZ55o&list=PLoROMvodv4...
3. CS330 Meta Learning https://www.youtube.com/watch?v=0rZtSwNOTQo&list=PLoROMvodv4...
Those will get you on track with general concepts about reasoning, AI engineering and concepts of learning itself
Language models for me a bit of headache because there're in different domain on intersection with linguistics and humanities but here's a good course
https://www.youtube.com/watch?v=rha64cQRLs8&list=PLoROMvodv4...
Those are all free and high-quality but require a lot of brain power
Good to see Chelsea Finn end up at Stanford. I had breakfast with her and her parents in 2013 when she was an undergraduate at MIT and it was fun to hear what options she was thinking about for her career.
I took a look at the course outline, and except for AutoML, it appears to be a one stop shop for learning multi-task and meta-learning. I just bookmarked the lectures on youtube.