The Kaggle Book

Spis treści książki

• Preface
• Part I: Introduction to Competitions
• Introducing Kaggle and Other Data Science Competitions
  • The rise of data science competition platforms
    • The Kaggle competition platform
      • A history of Kaggle
    • Other competition platforms
  • Introducing Kaggle
    • Stages of a competition
    • Types of competitions and examples
    • Submission and leaderboard dynamics
      • Explaining the Common Task Framework paradigm
      • Understanding what can go wrong in a competition
    • Computational resources
      • Kaggle Notebooks
    • Teaming and networking
    • Performance tiers and rankings
    • Criticism and opportunities
  • Summary
• Organizing Data with Datasets
  • Setting up a dataset
  • Gathering the data
  • Working with datasets
  • Using Kaggle Datasets in Google Colab
  • Legal caveats
  • Summary
• Working and Learning with Kaggle Notebooks
  • Setting up a Notebook
  • Running your Notebook
  • Saving Notebooks to GitHub
  • Getting the most out of Notebooks
    • Upgrading to Google Cloud Platform (GCP)
    • One step beyond
  • Kaggle Learn courses
  • Summary
• Leveraging Discussion Forums
  • How forums work
  • Example discussion approaches
  • Netiquette
  • Summary
• Sharpening Your Skills for Competitions
• Competition Tasks and Metrics
  • Evaluation metrics and objective functions
  • Basic types of tasks
    • Regression
    • Classification
    • Ordinal
  • The Meta Kaggle dataset
  • Handling never-before-seen metrics
  • Metrics for regression (standard and ordinal)
    • Mean squared error (MSE) and R squared
    • Root mean squared error (RMSE)
    • Root mean squared log error (RMSLE)
    • Mean absolute error (MAE)
  • Metrics for classification (label prediction and probability)
    • Accuracy
    • Precision and recall
    • The F1 score
    • Log loss and ROC-AUC
    • Matthews correlation coefficient (MCC)
  • Metrics for multi-class classification
  • Metrics for object detection problems
    • Intersection over union (IoU)
    • Dice
  • Metrics for multi-label classification and recommendation problems
    • MAP@{K}
  • Optimizing evaluation metrics
    • Custom metrics and custom objective functions
    • Post-processing your predictions
      • Predicted probability and its adjustment
  • Summary
• Designing Good Validation
  • Snooping on the leaderboard
  • The importance of validation in competitions
    • Bias and variance
  • Trying different splitting strategies
    • The basic train-test split
    • Probabilistic evaluation methods
      • k-fold cross-validation
      • Subsampling
      • The bootstrap
  • Tuning your model validation system
  • Using adversarial validation
    • Example implementation
    • Handling different distributions of training and test data
  • Handling leakage
• Modeling for Tabular Competitions
  • The Tabular Playground Series
  • Setting a random state for reproducibility
  • The importance of EDA
    • Dimensionality reduction with t-SNE and UMAP
  • Reducing the size of your data
  • Applying feature engineering
    • Easily derived features
    • Meta-features based on rows and columns
    • Target encoding
    • Using feature importance to evaluate your work
  • Pseudo-labeling
  • Denoising with autoencoders
  • Neural networks for tabular competitions
  • Summary
• Hyperparameter Optimization
  • Basic optimization techniques
    • Grid search
    • Random search
    • Halving search
  • Key parameters and how to use them
    • Linear models
    • Support-vector machines
    • Random forests and extremely randomized trees
    • Gradient tree boosting
      • LightGBM
      • XGBoost
      • CatBoost
      • HistGradientBoosting
  • Bayesian optimization
    • Using Scikit-optimize
    • Customizing a Bayesian optimization search
    • Extending Bayesian optimization to neural architecture search
    • Creating lighter and faster models with KerasTuner
    • The TPE approach in Optuna
  • Summary
• Ensembling with Blending and Stacking Solutions
  • A brief introduction to ensemble algorithms
  • Averaging models into an ensemble
    • Majority voting
    • Averaging of model predictions
    • Weighted averages
    • Averaging in your cross-validation strategy
    • Correcting averaging for ROC-AUC evaluations
  • Blending models using a meta-model
    • Best practices for blending
  • Stacking models together
    • Stacking variations
  • Creating complex stacking and blending solutions
  • Summary
• Modeling for Computer Vision
  • Augmentation strategies
    • Keras built-in augmentations
      • ImageDataGenerator approach
      • Preprocessing layers
    • albumentations
  • Classification
  • Object detection
  • Semantic segmentation
  • Summary
• Modeling for NLP
  • Sentiment analysis
  • Open domain Q&A
  • Text augmentation strategies
    • Basic techniques
    • nlpaug
  • Summary
• Simulation and Optimization Competitions
  • Connect X
  • Rock-paper-scissors
  • Santa competition 2020
  • The name of the game
  • Summary
• Leveraging Competitions for Your Career
• Creating Your Portfolio of Projects and Ideas
  • Building your portfolio with Kaggle
    • Leveraging Notebooks and discussions
    • Leveraging Datasets
  • Arranging your online presence beyond Kaggle
    • Blogs and publications
    • GitHub
  • Monitoring competition updates and newsletters
  • Summary
• Finding New Professional Opportunities
  • Building connections with other competition data scientists
  • Participating in Kaggle Days and other Kaggle meetups
  • Getting spotted and other job opportunities
    • The STAR approach
  • Summary (and some parting words)
• Other Books You May Enjoy
• Index

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