Yes! Any tiny errors (even punctuation) are super useful! Couldn't find this though. Can you give me more info about which sentence?

In page 41 just near problem 3.9 you write twice the. Do you need this type of comment too ?

I can't give an example material outside of just huggingface documentation but it's the big thing right now to leverage pre trained models so if your book doesn't mention it then it's missing the hipest thing. And also adapterhub.

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Not in Task Manager, but at least something would show up in GPU-Z like a clock increase over idle, memory bus usage, GPU utilisation, thermals, power draw, etc etc.

Racial criteria for being eligible to participate? Just when you think society is making progress beyond race...

Sounds great, thanks!

Can you give me an example of a review article or chapter in another book that covers roughly what you expect to see?

Yup -- some of it is a bit out of date now, but the stuff on probabilistic/graphical models is all still good and so is the geometry.

There are five chapters and around 100 pages. I think it would be a good start.

Yeah -- I feel a bit bad about that, but as someone else pointed out, the title is not actually the same. I should put a link to this book on my website though, so anyone looking for this book can find it.

I'm planning to add extra material on line for things like this where it's still unclear how important they are. If they get widely adopted, I'll incorporate into next edition.

I will release solutions to about half of them. Have to keep the rest back for professors. You can always message me if you want to know the solution to a particular problem.

I'm not sure what you mean by applying a NN to linear regression.

I'll try wording it differently. Sometimes a NN can outperform linear regression on regression problems, like in the example if there's a nonlinear relationship between some features and car price. But neural networks are also prone to over-fitting so I recommend against having a NN as one's first attempt to model some data. I recommend starting simple and trying complex models when it gets difficult to improve results in simple models.

I didn't say this before but another benefit of starting simple is that linear regression is usually much faster than neural networks, so you can iterate faster and try out more ideas quickly.

Didn‘t Hinton just try to start talking about it again at NeurIps? Isn‘t it like super irrelevant right now or am I missing something?

Many thanks for sharing

Probably the input query does not follow the pre-defined format. Do you mind opening an issue on GitHub?

I get the following error: SyntaxError: Unexpected token 'I', "Internal S"... is not valid JSON

Hi there, the demo is ready. Check it out!

https://huggingface.co/spaces/Spico/paper-hero

This probably: http://www.computervisionmodels.com/

That's interesting. I am a bit skeptical about some of the URM criteria but well, it would be difficult to do something perfect anyway.

That one’s full title is actually “ Understanding Deep Learning: Application in Rare Event Prediction”

I really like the book for it provides many beautiful pictures and gives us many intuitions behind deep learning algorithms, really appreciate the work you have done on this book.

Fitst impression base on transformers figure : You book could become a best seller…

Will be please to look at it, especially the figure explaining algorithms, Thanks