@leocd notebooks cover data exploration, data selection & baseline.
- Exploration, Body Paint Color Dominance, Image Transforms
- My Experiment Results + Baseline too I guess
- Ways to Select Which Data to Purchase - Episode 1
@gaurav_singhal has been super active with these notebooks and explainers! He even created a video tutorial for creating baseline! Don’t miss out on this!
- Apply the right Normalisation transformation for your data
- Create your baseline with 0.4+ on LB (Git Repo and Video)
- WANDB - Build better models faster with experiment tracking
- A New Baseline With 0.71 accuracy on LB
- [Explainer + Baseline] Get your Baseline right! (+0.84 LB)
- Explainability: How does your model actually learn?
@moto notebook on colour normalisation and baseline is also interesting
- Baseline + Exploration: random purchase vs full purchase
- Color normalization
- Baseline with score of 0.84
- Baseline with training done on all data, no data left for validation
Did these notebooks and explainers help you? Don’t forget to like the notebook and leave a if you found these resources interesting.
Don’t forget to submit your own notebook or resource for the Community Contribution Prize.
Scroll down for more details
Notebooks, Blog Posts, Tutorials, Screencasts, Youtube Videos, or even your active responses on the challenge forums - everything is eligible for the Community Contribution Prizes. We are looking forward to seeing everything you create!
The prizes typically go to individuals or teams who are extremely active in the community, share resources - or even answer questions - that benefit the whole community greatly!
You can make multiple submissions, but you are only eligible for the Community Contribution Prize once. In case of resources that are created, your work needs to be published under a license of your choice, and on a platform that allows other participants to access and use it.
- Data Purchasing Challenge: Notebook Submission
- Full list of available pre-trained weights
- Learn How To Make First Baseline Model With 0.44+ Accuracy on LeaderBoar
- Learn How To Setup MLOps (WandB) Pipeline To Track Your Experiments In 15 Minutes
- Discussions on Experiments with “unlabelled” data
- Discussions on Allowance of Pre-trained Model