Share your work here ✅

I started working through the first Lectures and wanted to share my initial project. As a Subaru fan, I wanted to create a classifier that distinguishes between two of the models, Forester and XV, which happen to look somewhat alike (being that one is a cross-over and the other a compact SUV). This and the fact that there are several generations of the models make the task at least mildly interesting/non-trivial.
Here is a preview of the data:


1. Initial Learning Rate Estimation and Model Training

Note that at this point we are already doing quite well.

*2. Unfreeze and Continuously Check Progress/Re-estimate LR

Things are still going well (no overfitting!) so we will do more

Now we seem to have ended up with a good set of weights
So let’s see the confusion matrix … lol
Selection_7
3.Conclusion We were able to achieve an excellent solution thanks to transfer learning (using a ResNet50 architecture) and the incredible fastai library. Overall we had just 616 data points, which definitely contradicts the common belief that one needs a lot of data to successfully use Deep Learning in practice.
The full notebook and the data are available at: ClassifyingSubarus
I am looking forward to exploring further projects and getting more familiar with the inner workings of the library, thanks Jeremy and team for a great course and library!

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thanks, has anybody tried LIME on text data ?. Any other explainability tools / packages that is good.

Nice! I bet @aza, @radek and @britt would be interested - they’re working on this area too (https://stochasticlabs.org/portfolio/ai-animal-intelligence/).

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Thank you! How interesting! This is what I am working on too (marine mammals mostly, whales, dolphins and manatees), first step is to identify calls, then to classify them and finally (hopefully) maybe use NLP models to search for underlying structure (like they tried with lost human languages i.e. Linear A or Voynich Manuscript.)

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Thank you so much @jeremy for the heads up on this :slight_smile:

Natalija, your work is amazing. I know a little bit about what goes into hosting a deep learning model, to be able to put an end to end example like this is really impressive.

I am however even more excited about your post above :slight_smile: I think there would be a lot of overlap between our work and a lot of grounds to share :slight_smile: I don’t want to hijack this thread even further, let me please send you a PM :slight_smile:

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I want to share my second mini project from the 4th lesson.

I built a phishing classifier using fast.ai tabular data and the following dataset: https://data.mendeley.com/datasets/h3cgnj8hft/1

The dataset contains 48 features extracted from 5000 phishing webpages and 5000 legitimate webpages.

I obtained 98% of accuracy, outperforming benchmarks obtained with traditional ML algorithms used for phishing detection like Random Forest, SVM. For instance, the related paper to the dataset says: " The overall experimental results suggest that HEFS performs best when it is integrated with Random Forest classifier, where the baseline features correctly distinguish 94.6% of phishing and legitimate websites using only 20.8% of the original features." https://www.sciencedirect.com/science/article/pii/S0020025519300763#ec-research-data

So, like @jeremy said in the 4th lesson: "It’s not true that neural nets are not useful for tabular data ,in fact they are extremely useful. "

I do really appreciate the work that Jeremy, Rachel and folks from the fast.ai team are doing to bring AI for all!

Update: Here is the notebook: https://github.com/johnagr/Phishing-Classifier-

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Part 1: Lesson 1 - Adventures
I used 1675 images of men and women and built a model to predict man vs woman. I good pretty good results. Here’s my work:

Screenshot%20from%202019-12-06%2000-14-37

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I am so positive this was a ton of work, but if you get around to sharing your source code. I would love to learn how you approached this problem outside what Jeremy taught on the Rossman data set. :blush:

If not maybe explaining did you tweak the architecture?

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Hi Johnpal
Nice work!
mrfabulous1 :smiley::smiley:

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to identify calls, then to classify them and finally (hopefully) maybe use NLP models to search for underlying structure

You may also be interested in this new 8TB dataset of whale calls I stumbled on the other day http://www.soest.hawaii.edu/ore/dclde/dataset/

DCLDE being the 2020 Workshop on Detection, Classification, Localization and Density Estimation of Marine Mammals using Passive Acoustics

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Hello Jeremy, in fact it wasn’t. I used the tabular notebook from the class. I added a few lines mostly to pre process the dataset and used the one cycle policy.

I was going to share the notebook in a github repository, but I had to leave home.

I will share it on Monday.

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Really?!

Awesome I have seen some amazing things happening with the fastai tabular framework. A winning solution for one of the kaggle competition that used k-folds and haven’t been able to completely go through that frame work, but it’s on my list.

I have worked with the tabular model and used the embedding to get better results with a random Forest, but look forward to being able to see your approach. I still think there is so much value in tabular data. Even thought nlp is what fascinates me the most.

Best Regards I’ll be back Monday night to see if you were able to upload thanks a million

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Interesting, thank you, I will check it up!

Happy to have something worthwhile to share here!

Check out Learning to Feel, which uses fastai to identify and extract emotions and mood from music / audio – built on top of Streamlit.

Any feedback is more than welcome :blush:

Github here.

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Hi zache hope all is well!

I ran your app I can see it took a lot of work.
Well done!

Cheers mrfabulous1 :smiley::smiley:

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A bit :slight_smile: Thank you!

Hi everyone,

This has probably been done many times before, but … I’ve written a web app to create and run inference on MNIST style images;


any feedback would be welcome (o:

Hi everyone,

I’m currently working through a first pass of both Part I & II and wanted to share my first project - mostly to say thank you to fast.ai for such a great course.

The use case is classification of New Zealand birds, but the main aim was to get it running end-to-end to understand what that involved. [mostly this :thinking:]

Cheers
Kristin

fastai-project
https://birdid-ui.azurewebsites.net/

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Hi kristinwil
Well done!

mrfabulous1 :smiley::smiley:

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This is awesome. Your website is also very nice. Quick question: how did you prepare your twitter data?