Lesson 8 - Official topic

Does “deleting an activation” mean setting it to zero?

It just sets it to zero.


Hinton’s intuitions of dropout (he has two reason behind it :slight_smile: ):


For dropouts, if the unit was set to zero during training, what weights is being used during test?

Dropout is only applied during training.

ok, but then for testing, which weights does it use if they were set to zero?

No weights were set to zero. Dropout is applied to activations.

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A general version of dropout was also proposed much earlier (Hanson 1990) but is rarely cited.

You can find many more instances of these “we or someone else did it first” claims on Schmidhuber’s blog, and here’s a rebuttal from Hinton.

EDIT: rather than adding another post I’m responding here :slight_smile: I’m inclined to agree that it’s a bit of a stretch (in addition to some of the other claims on that blog post). However, Hanson did also co-author another paper in 2018: Dropout is a special case of the stochastic delta rule: faster and more accurate deep learning.


Thanks for sharing. It seems more and more that Schmidhuber and his team or other groups did everything already but that’s a discussion for another day! :wink:


Will back propagation not update weights twice as it is referred twice in the same network?

Why does NLP not use CNNs? Is it because we can’t ‘label’ a body of text?

Here is the questionnaire for chapter 12. It is still a work in progress. Feel free to contribute!:

End of part 1 :frowning:


Thank you for pulling off an amazing class under historical circumstances! Thank you, Rachel, Sylvain, and Jeremy! Thank you, Thank you, Thank you!! And thank you for making us wear masks!


Hanson’s paper seems to be about adding stochastic noise to improve convergence.


You have change the dimensionality in CNN to a 1D CNN. https://pytorch.org/docs/stable/nn.html. 1D CNNs capture the order of the text as well. I have seen them as way of prepossessing.

Are there pointers for easier research papers to implement for DL starters ?

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I’ve also created a Wiki: 2020 ML Interviews Resources & Advice(s), please contribute!


Definitely suggest kaggle competitions. They can have data separated in other ways besides random split.

Good to get in project groups as well.

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Will be released a certification, or something to rember this great journey, signed by Jeremy?