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Machine Learning — Lecture 7 1:24:30

[02:14] Today we'll cover gradient descent and why the learning rate matters more than you think.

[08:41] The key insight is that a learning rate too high causes divergence, while too low gets stuck in local minima.

[16:33] Let me show you Adam optimizer — it adapts the learning rate per parameter, which is why it works so well in practice.

[22:47] Now, backpropagation through the network...

AI Chat
What's the difference between SGD and Adam?
Based on the lecture [08:41], Adam adapts the learning rate per parameter using momentum estimates, while SGD uses a fixed rate for all. The professor recommends Adam for most practical cases.
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