A rigorous, well-organized primer on meta-learning that walks through metric-based, model-based, and optimization-based approaches (including MAML and memory-augmented networks) with the math and intuition — useful grounding for anyone building models that adapt quickly from few examples.
[Updated on 2019-10-01: thanks to Tianhao, we have this post translated in Chinese !]
Transcript
[Updated on 2019-10-01: thanks to Tianhao, we have this post translated in Chinese !]
