noted

Thank you very much! :)

Thanks!

You're welcome!

What is the exact version you are using? 4.7-1.1 has err.rate. You can see it in the documentation here: cran.r-project.org/web/packages/randomForest/randomForest.pdf

:)

Thanks!

noted!

:)

I have a video about why we use n-1 when we estimate variance from a small set of data here: kzfaq.info/get/bejne/qa6Cdcpnp86vmn0.html

Thanks!

You have to start with some general sense of the variation in the data. It doesn't have to be perfect, but it's the best you can do.

I've got some code examples for how to use cross entropy in PyTorch here: lightning.ai/lightning-ai/studios/statquest-build-and-train-a-neural-network-with-multiple-inputs-and-outputs

:)

Thanks! Lots of work goes into these.

Those are the main ideas. The encoder can see all of the input at the same time, and the decoder can only see the output as it is generated. During inference, the encoder can process the the input in parallel and the decoder does its work sequentially. However, during training (which is the hard, time consuming part), the decoder can do its work in parallel using something called "masked self-attention", which I describe in the "decoder-only transformer" video: kzfaq.info/get/bejne/mLdlddKg0b6dcZs.html

Thanks!

:)

Thanks! :)

For PCA, this is just convention. However, it has roots in how variance is estimated in general, which I try to explain here: kzfaq.info/get/bejne/qa6Cdcpnp86vmn0.html

Thank you very much! :)

bam!

Thanks! 😃

Not yet. The best I can do is give you a link: online.stat.psu.edu/stat415/lesson/1/1.3

bam! :)

Thanks!

Glad it's helpful! :)

Sorry to hear that. It might be helpful if you watched this one first: kzfaq.info/get/bejne/fqpzgriJqpmsfYE.html

Thank you very much!

Bam! :)

There may be methods that can just determine protein-bound regions, in a general sense.

Good luck!

Thanks!

I talk about how to stack and use multiple LSTMs in my video on encoder-decoder networks: kzfaq.info/get/bejne/gp54ftqWv6-znZs.html

bam! :)

bam! :)

Thanks!

I think you could argue that this video is about NLP and is also a generative model, and I'll keep the other topic in mind.

​@@statquest I"ll explain myself better as I admit I phrased it poorly. For deep learning and machine learning you made amazing videos that covered the subjects from basic aspects to advanced ones - thus essentially teaching the whole subject in a fun, creative & enjoyable sequence of videos that can help beginners know it from top to bottom. However, for NLP for example you did talk about specific subjects like word embedding or auto-translation, but there are other topics (mostly older things) in that field that are important to learn such as n-grams & HMM. So my question was not only about specific advanced topics that connect to others, but rather about a full course that covers the basics of the subject as well. Sorry for my bad phrasing and thank you both for your quick answer and amazing videos! 😄

@@yuvalalmog6000 I hope to one day cover HMMs.

bam! :)

noted

That's pretty funny. Bam?

Glad I could help!

bam! :)

Sorry about that. You'll start learning about those in the next video.

Hahaha! BAM! :)

I don't believe that there is one, but you can plot the residuals and see if you see a bias there (the residuals should be normally distributed - and you can test that with something called a K-S Test).

bam! :)