[Resources] πŸ—‚ Notebooks Created By Community for Round 2

:wave: Hello,

Data Purchasing Challenge Round 2 is going strong :muscle:t3: To help you make the most of the challenge, we have released a new baseline containing fast heuristic implementations of some simple ideas.


Need some new ideas? Check out these excellent resources created by the community :arrow_down:

:bulb: Under the spotlight: Helpful resources created by challenge participants

If these notebooks and explainers help you, don’t forget to :heart: the notebook and leave a comment.

  1. Representation Learning: In his notebook, aorhan explains how Representation Learning can be utilised for data label purchase aka Active Learning (AL). You can find the complete explainer over here.

  2. Explainability: How does your model actually learn? Another notebook by aorhan, explain how given a sample you can identify what features contribute to the decision. And how you can improve your model. Read the notebook over here.

  3. Purchase with data anamoly: In this notebook, moto shows how to use anomaly scores to select images to buy. His notebook explains the approach and the result of his experiments.

  4. Labels co-occurence & Image Similarity: In this explainer santiactics performs label co-occurence analysis & Image Similarity using image embeddings. Read the notebook over here.

  5. Sneak peek into the image sample of Round 2: Taking a data visualisation approach sagar_rathod notebook visualises images from different classes and combinations of them. Complete notebook over here.

  6. :movie_camera: Additional Resources from Challenge Organisers and Top Participants: We recently hosted a live Town Hall event where the organisers and participants shared their ideas. You can find the recording & resource compilation over here.

:hourglass_flowing_sand:Don’t forget to submit your own notebook or resource for the Community Contribution Prize.

:raising_hand_woman: Do you have questions about the notebooks & baseline? Drop a comment on this thread to them answered quickly. What approach will you try? Let us know :arrow_down_small: