
Annotated notes to Fast.ai's lesson on Image Classification - zerosingularity
https://www.zerotosingularity.com/blog/fast-ai-part-1-course-1-annotated-notes/
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thisisananth
I'm taking this course now. Your annotations explaining each command are very
useful. Thanks for sharing them

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kevinb15
Here ([https://medium.com/@hiromi_suenaga/deep-
learning-2-part-1-le...](https://medium.com/@hiromi_suenaga/deep-
learning-2-part-1-lesson-1-602f73869197)) is another set of really detailed
notes. There is one for every video and they are also very good.

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zerosingularity
These notes are also really good, and ahead of what I have written up so
far... :)

My take on these is adding somethings I picked up along the way to add another
perspective and some additional info.

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needz
I'm working on an image classifier to identify pinball machines by their
backglass art. I've been using Turicreate. I can't find any comparisons of the
libraries (turicreate vs fast.ai). Does anyone have any resources that
compare/contrast the different solutions available for image classification?

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zerosingularity
I only learned about Turicreate after watching the CreateML video from WWDC.
It's on my list to play around with, but no comparisons so far.

Any feedback on using it?

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needz
I'm not experienced enough with ML to provide great feedback, but I did start
getting segfault errors when I trained on too many images. I have multiple
pictures of nearly 1000 different pinball machines, so right now it's a lot of
trial/error as I determine how many pictures of each backglass I should train
on and how diverse of angles/lighting produce the most accurate model. Right
now I'm at about 15 different pinball machines, each with 20-30 pictures, and
I get around 85% accuracy using my test set. I suspect/worry I won't be able
to make a good model that can identify 1000 different pinball machines, but my
patrons have asked for it so I'm giving it my all.

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zerosingularity
It might be an idea to train the same data with fast.ai and see where it leads
you. If you have the data in the right structure, it should not take too long.
Let me know if you have questions... :)

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amelius
> build a world-class image classifier in three lines of Python

How is that learning how to build something, as opposed to simply using a
system someone else built?

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sp332
Fast.ai's courses start at a high level with working code right off the bat.
Then they break it down and show you how the parts work. It's much more
motivating when you don't have to wait a long time to see any results. [Edit:
and you know you're going to get something good instead of putting a lot of
work in and getting something mediocre or plain not working.]

See this paragraph in the article:
[https://www.zerotosingularity.com/blog/fast-ai-
part-1-course...](https://www.zerotosingularity.com/blog/fast-ai-
part-1-course-1-annotated-notes/#the-practical-top-down-approach-of-fast-ai)

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sudoaza
A coworker has insisted on Fast.ai do you think its preferable over
Keras/TFLearn?

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zerosingularity
I found/find fast.ai to be incredibly useful for its practicality, good
results, and top-down approach, however, it is sometimes hard to reproduce the
results as well as clearly distilling what it is you can actually learn from
each lesson. Writing this post blew my socks off as to what was taught in the
video, yet it took me quite some time to get it all. So I hope the posts help
people with that aspect of the course.

At the moment, I'm learning fast.ai/PyTorch in parallel with Keras/Tensorflow,
so at this point, I have no definitive answer to your question which one is
preferable. It will probably depend and they will most likely have their own
benefits (I know that the boring answer, but I need to get more experience to
give you a better answer).

As an exercise I'm trying to write the fast.ai notebooks in Keras, to see how
they stack up. Might need to do a post on that as well.

I hope to answer your question better in the future. Could you tell me more
about what you want to achieve, I might be of more assistance?

