You can run these notebooks on cloud platforms like Kaggle Colab or your local machine. Note that most chapters require a GPU even TPU sometimes to run in a reasonable amount of time, so we recommend one of the cloud platforms as they come pre-installed with CUDA.
To run these notebooks on a cloud platform, just click on one of the badges (Colab or Kaggle) in the table below. The code will be reproduced from Github directly onto the choosen platform (you may have to add the necessary data before running it). Alternatively, we also provide links to the fully working original notebook on Kaggle that you can copy and immediately run.
no | Chapter | Notebook | Colab | Kaggle |
---|---|---|---|---|
05 | Competition Tasks and Metrics | meta_kaggle | ||
06 | Designing Good Validation | adversarial-validation-example | ||
07 | Modeling for Tabular Competitions | interesting-eda-tsne-umap | ||
meta-features-and-target-encoding | ||||
really-not-missing-at-random | ||||
tutorial-feature-selection-with-boruta-shap | ||||
08 | Hyperparameter Optimization | basic-optimization-practices | ||
hacking-bayesian-optimization-for-dnns | ||||
hacking-bayesian-optimization | ||||
kerastuner-for-imdb | ||||
optuna-bayesian-optimization | ||||
scikit-optimize-for-lightgbm | ||||
tutorial-bayesian-optimization-with-lightgbm | ||||
09 | Ensembling with Blending and Stacking Solutions | ensembling | ||
10 | Modeling for Computer Vision | augmentations-examples | ||
images-classification | ||||
prepare-annotations | ||||
segmentation-inference | ||||
segmentation | ||||
object-detection-yolov5 | ||||
11 | Modeling for NLP | nlp-augmentations4 | ||
nlp-augmentation1 | ||||
qanswering | ||||
sentiment-extraction | ||||
12 | Simulation and Optimization Competitions | connectx | ||
mab-santa | ||||
rps-notebook1 |
- Introducing Kaggle and Other Data Science Competitions
- Organizing Data with Datasets
- Working and Learning with Kaggle Notebooks
- Leveraging Discussion Forums
- Competition Tasks and Metrics
- Designing Good Validation
- Modeling for Tabular Competitions
- Hyperparameter Optimization
- Ensembling with Blending and Stacking Solutions
- Modeling for Computer Vision
- Modeling for NLP
- Simulation and Optimization Competitions
- Creating Your Portfolio of Projects and Ideas
- Finding New Professional Opportunities