Increase the learning rate of your optimizer. Something else you could try— look into “back propagation” to tune your learning rate after each layer.

I don’t get the last question when V(x_t) = 1 means that beta_t = 1

Why the confusion?

It's still working,

I started downloading it 4 hours ago and it has finished a few minutes ago.

Give up

Start Over

That link has been working fine for me. You're probably just copying wrong. Here:

magnet:?xt=urn:btih:ZXXDAUWYLRUXXBHUYEMS6Q5CE5WA3LVA&dn=LLaMA

Mods let me know if this is not allowed.

Let me know if you get a link

thanks!

Work on your ability to communicate?

From (1), it looks like V(x_t) is the conditional variance of x_t given x_{t-1} (for the forward process defined by q).

yes t is the timestep index

Do you mean at different time steps?

The variance of your sample x_t, simple as that.

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I just bought a used 3090 for $740 on eBay before tax. I view my GPU as a for-fun expenditure. Part of that is ML stuff. For the cost of a handful of new release videogames, you can go from 10gb to 24gb and do a lot of cool stuff. There's going to be increasingly less state of the art stuff that fits in 10gb comfortably.

Check out the WaveNet paper for some ideas about this!

Yep feel free

Thanks!! I really appreciate. I really like the ai world and if it's not too much to ask, if you have anything else you would recommend me to go further I would appreciate it. Again thanks!!

If you’re only detecting speech, that is doable with heuristics and some napkin math, or an MLP, for simple cases. However, “detect speech in this audio” is rarely the end of the story in the real world. Next up comes transcription, sentiment analysis, tonal feature flagging, etc. all of which are currently dominated by Transformers. You’ll also see some great work in the RNN space, but Transformer-based architectures are king right now.

Some models for inspiration, https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&sort=downloads

Thanks for that link!! But out of curiosity, what would you use instead of a MLP?

MLP for speech recognition probably isn't a great solution, but if it's for a class and you can only use numpy start here: https://towardsdatascience.com/coding-a-neural-network-from-scratch-in-numpy-31f04e4d605

Well to be honest, unless there's some particular reason why you need the GPUs locally, the most cost effective solution is to just run it in the cloud.

Having GPUs locally is mostly a luxury for when some contract prevents you from using the cloud, or you need to train something every day for several hours over a year or more. For everything else, cloud pay-as-you-go will be cheaper and faster.

Yeah, I found out about gpu bus width a few hours ago. At least I’be learned a lot about GPUs haha

Buying a 3090 when my current 6gb laptop gpu stops working for my purposes.

The 3060s are pretty gimped for DL. Currently, you either go for a 3090/4090, or used 1080Ti/2080Ti(s) if on a budget.