Any particular reason for moving from CNN to RNN?

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So I'm working with the UrbanSound 8k set on Kaggle.

I want to try two approaches:

• MFCCs and Mels for image classification.
• Raw audio data classification.

Would a 1DCNN work for both approaches?

Oh, definitely, it's an amazing optimization.

But less than a token a second is going to be too slow for a lot of real time applications like human chat.

Still, very cool though

Do I need a specific GPU generation for 4bit weights? Or just anything that supports tensorflow/pytorch?

Thanks! Will add citation info there!

Thanks! Following your suggestion I posted to r/DataScience

Thanks!

Thanks!

Thanks!

Thanks!

Cool!

Yes. I think confidence intervals assume iid. If they are not iid, then the CI could be too short.

Try rwkv 0.4.0 & latest ChatRWKV for 2x speed :)

It’s fine, open source SOTA will make them irrelevant sooner rather than later

seriously, how much would you pay if such a robot exist say can do weeding, digging, sowing seeds, irrigation. Would you buy it?

They managed to run the 7B model on a Raspberry PI and a Samsung Galaxy S22 Ultra.

Hey, I'm new at ML and I have a question. I've created a LSTM and XGBoost model etc, trained it, evaluated it etc. But now, how do I actually forecast future data ? Do you have a notebook where the creator actually plot predictions? I can't seem to find one !

I'm not actually sure. I've just been chatting with people in an unrelated Discord's off topic channel about it.

I'd post some of what I've got from it but I have no idea what I'm doing with it and don't think what I'm getting would be decently representative of what it can actually do.

How difficult and time consuming is it to teach AI how to choose glasses according to the type of face with tools we have today?

see previous response

There was a paper about it. There was a find - specific set of positions not encountered or pooply represented during self-play. Fully trained AlphaGo was failing on those positions. However then they were explicitly added to the training set the problem was fixed and AlphaGo was able to play them well. This adversarial traning seems just an automatic way to find those positions.

PS fintess landscape is not convex it separated by hills and valleys. Self-play may have a problem in reaching all important states.

On which metric are you basing this on? I'm not deep in ASR but in the Whisper paper it is compared to word2vec 2.0 and whisper is better in most categories.

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Any idea for finetuning llama on multi-gpu setup?