Deep Learning

Course Content

Total learning: 3 lessons

Practical Aspects of Deep Learning

  1.  Train / Dev / Test sets
  2. Bias / Variance
  3. Basic Recipe for Machine Learning
  4. Regularization
  5. Why regularization reduces overfitting?
  6. Dropout Regularization
  7. Understanding Dropout
  8. Other regularization methods
  9. Normalizing inputs
  10. Vanishing / Exploding gradients
  11. Weight Initialization for Deep Networks
  12. Numerical approximation of gradients
  13. Gradient checking
  14. Gradient Checking Implementation Notes
  15. Notebook: Initialization
  16. Notebook: Regularization
  17. Notebook: Gradient Checking
  18. Yoshua Bengio interview
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