Deciding What to Do Next (Revisited)

Deciding What to Do Next (Revisited)
  • What to do or not to do to increase the learning algorightm
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Learning Curves

Learning Curves
  • Curve to check the learning algorithm, whether bias, variance, or both
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Regularization and Bias/Variance

Regularization and Bias/Variance
  • Regularization can avoid underfitting/overfitting. But how it does acttually affect the learning algorithms
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Diagnosing bias vs. variance

Diagnosing bias vs. variance
  • Most pitfall in machine learning is (bias)underfitting vs (variance)overfitting problems
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Model selection and training/validation/test sets

Model selection and training/validation/test sets

Evaluating a hyphotesis

Evaluating a hyphotesis
  • Choosing the correct parameters whether is underfitting or overfitting
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Deciding what to Try Next

Deciding what to Try Next
  • Should be expected by now average expert of machine learning
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Autonomous Driving (Examples)

Autonomous Driving (Examples)

Putting it together

Putting it together
  • Implement all pieces together to make overall process for neural networks learning algorithm
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Random Initialization

Random Initialization
  • The last pieces in neural networks to be implemented
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