吴恩达结构化机器学习项目课程总结

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Note

This is my personal summary after studying the course, Structuring Machine Learning Projects, which belongs to Deep Learning Specialization. and the copyright belongs to deeplearning.ai.

Content Table of my personal notes

week 1: 01_ml-strategy-1

• 01_introduction-to-ml-strategy
  • 01_why-ml-strategy
  • 02_orthogonalization
    • Orthogonalization
• 02_setting-up-your-goal
  • 01_single-number-evaluation-metric
  • 02_satisficing-and-optimizing-metric
    • summary
  • 03_train-dev-test-distributions
    • summary
  • 04_size-of-the-dev-and-test-sets
    • Summary
  • 05_when-to-change-dev-test-sets-and-metrics
    • summary
• 03_comparing-to-human-level-performance
  • 01_why-human-level-performance
    • summary
  • 02_avoidable-bias
    • summary
  • 03_understanding-human-level-performance
    • Summary
• 04_surpassing-human-level-performance
  • summary
• 05_improving-your-model-performance

week 2: 02_ml-strategy-2

• 01_error-analysis
  • 01_carrying-out-error-analysis
  • 02_cleaning-up-incorrectly-labeled-data
  • 03_build-your-first-system-quickly-then-iterate
• 02_mismatched-training-and-dev-test-set
  • 01_training-and-testing-on-different-distributions
  • 02_bias-and-variance-with-mismatched-data-distributions
  • 03_addressing-data-mismatch
• 03_learning-from-multiple-tasks
  • 01_transfer-learning
  • 02_multi-task-learning
• 04_end-to-end-deep-learning
  • 01_what-is-end-to-end-deep-learning
  • 02_whether-to-use-end-to-end-deep-learning