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Contents:

  • Part I: Applied Math and Machine Learning Basics
    • 5 Machine Learning Basics
      • 5.4 Estimator, Bias and Variance
      • 5.6 Bayesian Statistics
  • Part II: Modern Practical Deep Networks
  • Part III: Deep Learning Research
  • Extra
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  • 5 Machine Learning Basics
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5 Machine Learning BasicsΒΆ

  • 5.4 Estimator, Bias and Variance
  • 5.6 Bayesian Statistics
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