Lesson 12 (2019) discussion and wiki

Swift on older mac systems is not feasible ??

Feel better soon Jeremy and Rachel! (And welcome Chris!)


Thank you fastai team for this lesson! Jeremy, we saw your health made it harder than usual for you so thank you for pushing through it!


Great class as always! Thank you for all that are making this possible.


Couldn’t agree more, the ability to integrate machine learning with applications seamlessly is one of the biggest opportunities of S4TF and the reason I am interested in learning Swift/S4TF


Want to know about Mixup look here:https://www.inference.vc/mixup-data-dependent-data-augmentation/
From Link:
We take pairs of datapoints (x1,y1)(x1,y1) and (x2,y2)(x2,y2), then choose a random mixing proportion λλ from a Beta distribution, and create an artificial training example (λx1+(1−λ)x2,λy1+(1−λ)y2)(λx1+(1−λ)x2,λy1+(1−λ)y2). We train the network by minimizing the loss on mixed-up datapoints . This is all.

This is better Mixup data augmentation


What do you foresee will be the first state-of-the-art applications using Swift for Tensorflow? It would be nice if the new framework first focuses on a subset of deep learning that’s up and coming so it can gain traction with communities who are not as strongly committed to their old development tools. I guess if fastai builds the libraries in the right order, it could have a big influence in how people will adjust their research workflow.

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I tried to educate myself on torch.jit today, found a few entry level overviews that gave me good understanding of how/when to use jit.script and jit.trace:

Some advanced materials that I haven’t read yet:


We do not want to use Python… Only reason I think Python will last longer… is the echo system of Python… Pandas, Numpy, Matplotlib etc… Not sure how much time will Swift take to reach that level of echo system… To me Swift is as good as Java or Scala :wink: I know Swift is heavily used on iOS and OSX development but not sure it has robust ecosystem for datascience.

Question is can we do Swift programming in Jupyter notebook? Are all data science packages like Pandas, Numpy, Matplotlib etc… available in Swift…

Julia had been around since 2009, why was it not adopted for DL/ML instead of Python earlier ?.

This is where @Moody question about context came from. We are interested in contexts >20,000 tokens, still v.small for genomics. would be interested in hering people thoughts about how we engineer for such large contexts?

What sorts of things are you trying to model? Depending on the structure of the problem it might not be necessary to deal with 20k+ contigs simultaneously.

Please write out acronyms that are not so common that everyone knows them. What is NER?

What is TDD?

Test driven development - https://en.m.wikipedia.org/wiki/Test-driven_development


In this context, NER == Named-entity recognition. :slightly_smiling_face:

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BTW, added TDD to the glossary as soon as it was mentioned a few times in the Part 2 discussions.

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Yes and yes: there is support for swift in jupyter and an interopability with python libraries.


Thanks Sylvain. So you are saying we will do data analysis using the Python library we know of… Then for model training we will use Swift?

I always forget. Is parameters the things we learn and activation the things we calculate?
parameters = weights
actiovations = outputs of layer

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