Lesson 5 In-Class Discussion

Part 1 (2018)
44

https://www.quora.com/What-are-hyperparameters-in-machine-learning

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45

Do we need to check in from romote?

46

Send an email to Mindi and Leslie

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47

A note on collaborative filtering. Iā€™ve found that most people in the industry is using Spark or other distributed framework to do it because in most cases itā€™s a huge matrix decomposition (100M products x 10M customers for instance). Would we be able to use fastai directly or is there a way to customize that?

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48

How is this different from torch.mm(a,b)?

49

I donā€™t think theyā€™re any different, but Jeremy mentioned he wanted to try to avoid using more abstract libraries if possibleā€¦ so implementing it kind of from scratch to show the intuition behind it

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50

International fellows need to check in remotely?

51

No, you donā€™t have to.

52

Yes thatā€™s what I was thinking. a*b gives the element-wise product, not dot product. dot product would be torch.mm()

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53

Didnā€™t he sum it to make it dot product? I think it is similar to torch.mm().

54

The sum gave us a 2x1 matrix.

Dot product of 2x2 and 2x2 matrix should give us 2x2 matrix.

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55

where does n_factors come from in init()?

56

itā€™s a global variable in this notebook

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57

Thanks, seems like an odd way of passing it in ā€¦ but I guess itā€™s not a huge problem.

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58

@yinterian Do you have the link to the blog used for initialization?

59

Yes the dimensions donā€™t match

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60
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61

I agree, but I think itā€™s a consequence of the gap between working in a notebook for exploratory work, then packaging code up for reusability, deployment, and other software engineering goodness.

Jeremy (I think) tweeted out a link to Jake Vanderplasā€™ series called ā€œReproducable Data Analysis in Jupyterā€ that shows a reasonable workflow to move from one to the other. https://www.youtube.com/watch?v=_ZEWDGpM-vM&list=PLYCpMb24GpOC704uO9svUrihl-HY1tTJJ

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62

It may be this one

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63

Could class EmbeddingDot reuse the DotProduct class from before?

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