Early Stopping

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Stopping+Regularization+Dropout+Overfitting Youtube search... Stopping+Regularization+Dropout+deep+machine+learning+ML ...Google search

Good practices for addressing overfitting:

  • add more data
  • use Data Augmentation
  • use batch normalization
  • use architectures that generalize well
  • reduce architecture complexity
  • add Regularization
    • L1 and L2 Regularization - update the general cost function by adding another term known as the regularization term.
    • Dropout - at every iteration, it randomly selects some nodes and temporarily removes the nodes (along with all of their incoming and outgoing connections)
    • Data Augmentation
    • Early Stopping