Lesson 6 In-Class Discussion ✅

Use this thread for questions/discussion of today’s lesson. Please do not use this for questions/comments about topics we haven’t covered yet - use the Lesson 6 further discussion thread for that. Also remember to watch the official updates thread.


I hope FB releases windows (anaconda) version of pytorch 1.0 during NeurIPS


Beautiful landing page for platform.ai @jeremy!

what do the two axes in there represent?


Nothing really I think, just a 2D projection from a much larger space. A bit like PCA I guess. Well rather than nothing it’s more that “we don’t know” and we have to figure it out, that’s the whole point of it :slight_smile:

Have you tried peterjc123 builds. Working great for me.

I think I must have missed this - but what tool is Jeremy using to show the PCA-like projection?

This is platform.ai

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Maybe I missed first couple of minutes. Dumb question: What is projection ?

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where do these categories come from? are they pre-defined? or is there a set I can choose from (like if I want to find a projection that separates sedans from SUVs, is it possible?). Thanks.

A projection is a way of going from a higher dimensional space to a lower dimensional space. Although the new, low dimensions don’t have inherent meanings, often you can observe a meaning or pattern.

Often you many want to look at several possible projections to find something that seems “meaningful” or that separates your data in a way that interests you.


It’s there to help you label a dataset, so yes, you can have the labels you want.


I was part of the fellowship this past summer some of my fellow fellows built this! Pretty cool to see it live!


Does platform.ai employ active learning ?

The projections that Jeremy was demonstrating a couple of min back – are they some intermediate layer taken from a trained network or similar?
Or is this a standalone tool which just takes all images, similar to doing PCA on a high-dimensional data. ?

It involves taking intermediate layers from a network.

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Yes it is.

Does adding many more features always positive for a neural net, or can it lead to a kind of “information overflow” and actually be detrimental to the model ?

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Making life easier for your model will generally result in getting better results.
That being said, to quote last-week-Jeremy, try bla :wink: