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Deep Learning: Difference between revisions
→Theory of self-supervised learning
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# Case 1: H is a discrete variable then <math>g^*(x, z) = \eta^*(x)^t \psi(z^*)</math> | # Case 1: H is a discrete variable then <math>g^*(x, z) = \eta^*(x)^t \psi(z^*)</math> | ||
# Case 2: There exists <math>\eta,\psi</math> such that <math>E(\eta(x)^t \psi(z) - g^*(x,z))^2 \leq o(\frac{1}{m})</math>. | # Case 2: There exists <math>\eta,\psi</math> such that <math>E(\eta(x)^t \psi(z) - g^*(x,z))^2 \leq o(\frac{1}{m})</math>. | ||
==LSTMs and transformers== | |||
Lecture 23 (November 19, 2020) | |||
===Recurrent Neural Networks (RNNs)=== | |||
==Meta Learning== | ==Meta Learning== | ||