Loading video...
Video Failed to Load
gradient descent navigating the loss landscape
224,663 views • 8 months ago •via X (Twitter)
33 Comments

Lower the learning rate maybe, that's a lot of energy

LR too high. Adam might work.

or: GD getting stuck in a local minimum for a couple of epochs

That's what happens when mixing reward functions for robustness :)

This looks like the learning rate is too high

Try conjugated gradient descent :)

@TensorTwerker

perfect 😂

I see some Mathematics in there

great way to start the year fr

When k-means fails, you better call this guy. Saul never did anything for anybody. At least this guy solves an autograd problem in k-means!

You just don’t see this kind of content on LinkedIn.

If there was anything that i love more about car's is i love a challenge when driving and this is me in my mind's eye enjoying my self if it was me lol

Looks kinda fun tho

lol hahahaha

I think momentum is working quite a bit

🥲

Sochastic Gradient Descent 😭😭😭

@jaraxiong 😂😂

gradient descent nearing a truth that violates the guardrails cost function

why did I laugh at this😂

SWA

Damn I understand this joke. Am I normal?

Real😂

At least the AIs are having fun. Also, git bisect slow

only Nesterov Accelerated Gradient could work because the convex seems too smooth

lol

im so jaded. nothing impresses me anymore 😂

haven’t seen these in a while

Inaccurate

Elite ball knowledge required

You need to adjust learning rate

😂
