==================== Week 00 ====================
00. introducing/01. Web.mp4 	||	 min: 13, sec: 06
00. introducing/02. Welcome.mp4 	||	 min: 16, sec: 19
==================== Week 00 ====================
Total duration: 00:29:26
=================================================


==================== Week 01 ====================
01. introduction/1. Applications.mp4 	||	 min: 07, sec: 07
01. introduction/2. What is deep learning.mp4 	||	 min: 07, sec: 07
01. introduction/3. Inputs, outputs, and models of supervised learning.mp4 	||	 min: 26, sec: 23
01. introduction/4. Boundaries.mp4 	||	 min: 07, sec: 46
01. introduction/5. History.mp4 	||	 min: 10, sec: 55
01. introduction/6. Recap.mp4 	||	 min: 07, sec: 00
==================== Week 01 ====================
Total duration: 01:06:21
=================================================


==================== Week 02 ====================
02. prerequisites/01. preface.mp4 	||	 min: 02, sec: 09
02. prerequisites/02. logic intro.mp4 	||	 min: 00, sec: 22
02. prerequisites/03. sufficient condition.mp4 	||	 min: 04, sec: 52
02. prerequisites/04. not.mp4 	||	 min: 04, sec: 48
02. prerequisites/05. necessary condition.mp4 	||	 min: 05, sec: 06
02. prerequisites/06. different goals.mp4 	||	 min: 07, sec: 28
02. prerequisites/07. math intro.mp4 	||	 min: 01, sec: 12
02. prerequisites/08. vectors and matrices.mp4 	||	 min: 16, sec: 28
02. prerequisites/09. and 10. block multiplication.mp4 	||	 min: 12, sec: 57
02. prerequisites/11. functions.mp4 	||	 min: 12, sec: 39
02. prerequisites/12. norms for vectors.mp4 	||	 min: 07, sec: 03
02. prerequisites/13. norms for matrices.mp4 	||	 min: 12, sec: 09
02. prerequisites/14. derivatives.mp4 	||	 min: 10, sec: 50
02. prerequisites/15. partial derivative.mp4 	||	 min: 09, sec: 54
02. prerequisites/16. gradient.mp4 	||	 min: 13, sec: 43
02. prerequisites/17. second derivative.mp4 	||	 min: 09, sec: 40
02. prerequisites/18. taylor series.mp4 	||	 min: 06, sec: 54
02. prerequisites/19. chain rule.mp4 	||	 min: 09, sec: 21
02. prerequisites/20. computation graph.mp4 	||	 min: 23, sec: 18
02. prerequisites/21. vector space.mp4 	||	 min: 09, sec: 41
02. prerequisites/22. linear transformations.mp4 	||	 min: 16, sec: 25
02. prerequisites/23. affine transformation.mp4 	||	 min: 02, sec: 28
02. prerequisites/24. rank of a matrix.mp4 	||	 min: 05, sec: 46
02. prerequisites/25. range of a matrix.mp4 	||	 min: 04, sec: 48
02. prerequisites/26. nullspace and integral.mp4 	||	 min: 07, sec: 54
02. prerequisites/27. probability intuition for our tasks.mp4 	||	 min: 03, sec: 05
02. prerequisites/28. probability theory.mp4 	||	 min: 07, sec: 30
02. prerequisites/29. random variables.mp4 	||	 min: 09, sec: 01
02. prerequisites/30. bayes theorem.mp4 	||	 min: 08, sec: 48
02. prerequisites/31. independence.mp4 	||	 min: 07, sec: 56
02. prerequisites/32. Expected value and variance.mp4 	||	 min: 10, sec: 39
02. prerequisites/33. discrete and continuous.mp4 	||	 min: 01, sec: 42
02. prerequisites/34. pdf and pmf.mp4 	||	 min: 12, sec: 09
02. prerequisites/35. normal distribution.mp4 	||	 min: 14, sec: 14
02. prerequisites/36. convex sets intro.mp4 	||	 min: 01, sec: 04
02. prerequisites/37. affine set.mp4 	||	 min: 11, sec: 14
02. prerequisites/38. convex set.mp4 	||	 min: 06, sec: 17
02. prerequisites/39. convex hull and convex combination.mp4 	||	 min: 05, sec: 23
02. prerequisites/40. hyperplane and halphspace.mp4 	||	 min: 08, sec: 24
02. prerequisites/41. polyhedra and norm ball.mp4 	||	 min: 06, sec: 03
02. prerequisites/42. linear and affine functions.mp4 	||	 min: 03, sec: 55
02. prerequisites/43. operations that preserve convexity.mp4 	||	 min: 06, sec: 15
02. prerequisites/44. examples for affine functions.mp4 	||	 min: 09, sec: 07
02. prerequisites/45. separating hyperplane theorem.mp4 	||	 min: 03, sec: 50
02. prerequisites/46. supporting hyperplane theorem.mp4 	||	 min: 06, sec: 13
02. prerequisites/47. convex functions intro.mp4 	||	 min: 00, sec: 51
02. prerequisites/48. convex functions.mp4 	||	 min: 13, sec: 13
02. prerequisites/49. norms as convex functions.mp4 	||	 min: 05, sec: 26
02. prerequisites/50. first order condition.mp4 	||	 min: 05, sec: 11
02. prerequisites/51. first order condition example.mp4 	||	 min: 01, sec: 19
02. prerequisites/52. second order condition.mp4 	||	 min: 23, sec: 11
02. prerequisites/53. some definitions.mp4 	||	 min: 08, sec: 57
02. prerequisites/54. necessary and sufficient conditions.mp4 	||	 min: 07, sec: 54
02. prerequisites/55. investigating convexity.mp4 	||	 min: 03, sec: 14
02. prerequisites/56. maintaining the convexity of functions - part one.mp4 	||	 min: 09, sec: 19
02. prerequisites/57. maintaining the convexity of functions - part two (conjugate).mp4 	||	 min: 10, sec: 43
02. prerequisites/58. maintaining the convexity of functions - part three.mp4 	||	 min: 13, sec: 46
02. prerequisites/59. gradient descent intuition and formula.mp4 	||	 min: 17, sec: 15
02. prerequisites/60. directional derivative.mp4 	||	 min: 09, sec: 55
02. prerequisites/61. gradient direction.mp4 	||	 min: 04, sec: 07
02. prerequisites/62. level set.mp4 	||	 min: 06, sec: 00
02. prerequisites/63. intuition of optimization.mp4 	||	 min: 01, sec: 43
02. prerequisites/64. gradient descent.mp4 	||	 min: 19, sec: 41
02. prerequisites/65. subderivative intro.mp4 	||	 min: 05, sec: 21
02. prerequisites/66. subderivative method.mp4 	||	 min: 04, sec: 25
02. prerequisites/67. subderivative example.mp4 	||	 min: 06, sec: 46
02. prerequisites/68. understanding pseudocode.mp4 	||	 min: 14, sec: 27
02. prerequisites/69. paper.mp4 	||	 min: 01, sec: 53
02. prerequisites/70. projection.mp4 	||	 min: 02, sec: 15
02. prerequisites/71. recap and extra resources.mp4 	||	 min: 06, sec: 14
==================== Week 02 ====================
Total duration: 09:24:22
=================================================


==================== Week 03 ====================
03. logistic regression/01. preface.mp4 	||	 min: 02, sec: 22
03. logistic regression/02. inference intro.mp4 	||	 min: 00, sec: 23
03. logistic regression/03. inference.mp4 	||	 min: 07, sec: 41
03. logistic regression/04. train and test phases intro.mp4 	||	 min: 00, sec: 15
03. logistic regression/05. train and test phases.mp4 	||	 min: 11, sec: 01
03. logistic regression/06. a neuron.mp4 	||	 min: 06, sec: 51
03. logistic regression/07. inputs and outputs of a neuron.mp4 	||	 min: 04, sec: 58
03. logistic regression/08. notation.mp4 	||	 min: 12, sec: 19
03. logistic regression/09. logistic regression intro.mp4 	||	 min: 13, sec: 30
03. logistic regression/10. discrete loss function.mp4 	||	 min: 20, sec: 45
03. logistic regression/11. continuous outputs.mp4 	||	 min: 22, sec: 02
03. logistic regression/12. loss function design.mp4 	||	 min: 19, sec: 31
03. logistic regression/13. convex loss function for logistic regression.mp4 	||	 min: 12, sec: 42
03. logistic regression/14. bernoulli distribution.mp4 	||	 min: 11, sec: 38
03. logistic regression/15. cost function.mp4 	||	 min: 10, sec: 10
03. logistic regression/16. confusing graphs.mp4 	||	 min: 16, sec: 10
03. logistic regression/17. more detail for bias term.mp4 	||	 min: 09, sec: 12
03. logistic regression/18. gradient descent for loss function.mp4 	||	 min: 18, sec: 45
03. logistic regression/19. gradient descent for cost function.mp4 	||	 min: 07, sec: 13
03. logistic regression/20. one step of gradient descent.mp4 	||	 min: 18, sec: 43
03. logistic regression/21. how to have fast computations.mp4 	||	 min: 11, sec: 25
03. logistic regression/22. GPUs and vectorization.mp4 	||	 min: 14, sec: 32
03. logistic regression/23. vectorizing forward pass.mp4 	||	 min: 12, sec: 44
03. logistic regression/24. vectorizing backward pass.mp4 	||	 min: 18, sec: 46
03. logistic regression/25. vectorized gradient descent for logistic regression.mp4 	||	 min: 05, sec: 10
03. logistic regression/26. limitations intro.mp4 	||	 min: 02, sec: 49
03. logistic regression/27. limitations of logistic regression.mp4 	||	 min: 07, sec: 30
03. logistic regression/28. reccap and resources.mp4 	||	 min: 02, sec: 51
==================== Week 03 ====================
Total duration: 05:02:12
=================================================


==================== Week 04 ====================
04. linear regression/01. preface.mp4 	||	 min: 03, sec: 16
04. linear regression/02. general concepts.mp4 	||	 min: 07, sec: 30
04. linear regression/03. linear regression example.mp4 	||	 min: 05, sec: 03
04. linear regression/04. training intuition for linear regression.mp4 	||	 min: 02, sec: 38
04. linear regression/05. properties of lines.mp4 	||	 min: 02, sec: 36
04. linear regression/06. linear regression details - first part.mp4 	||	 min: 13, sec: 07
04. linear regression/07. linear regression details - second part.mp4 	||	 min: 11, sec: 19
04. linear regression/08. higher dimensions.mp4 	||	 min: 08, sec: 14
04. linear regression/09. MSE.mp4 	||	 min: 10, sec: 26
04. linear regression/10. MAE.mp4 	||	 min: 06, sec: 18
04. linear regression/11. absolute value function.mp4 	||	 min: 05, sec: 04
04. linear regression/12. solution finding.mp4 	||	 min: 11, sec: 05
04. linear regression/13. drawbacks.mp4 	||	 min: 10, sec: 08
04. linear regression/14. polynomial features.mp4 	||	 min: 07, sec: 20
04. linear regression/15. recap and resources.mp4 	||	 min: 04, sec: 37
==================== Week 04 ====================
Total duration: 01:48:48
=================================================


==================== Week 05 ====================
05. shallow neural networks/01. preface.mp4 	||	 min: 04, sec: 32
05. shallow neural networks/02. outline.mp4 	||	 min: 01, sec: 05
05. shallow neural networks/03. neural networks with one hidden layer.mp4 	||	 min: 15, sec: 13
05. shallow neural networks/04. some notes about shallow networks.mp4 	||	 min: 09, sec: 17
05. shallow neural networks/05. intuition of the decision boundaries for shallow networks.mp4 	||	 min: 03, sec: 34
05. shallow neural networks/06. hidden concepts.mp4 	||	 min: 10, sec: 52
05. shallow neural networks/07. vectorizing network operations - forward pass.mp4 	||	 min: 45, sec: 19
05. shallow neural networks/08. vectorization across samples - first part.mp4 	||	 min: 17, sec: 53
05. shallow neural networks/09. vectorization across samples - second part.mp4 	||	 min: 09, sec: 04
05. shallow neural networks/10. vectorization across samples - third part.mp4 	||	 min: 04, sec: 56
05. shallow neural networks/11. backprop with loss - part one.mp4 	||	 min: 12, sec: 20
05. shallow neural networks/12. backprop with loss - part two.mp4 	||	 min: 21, sec: 00
05. shallow neural networks/13. backprop with loss - part three.mp4 	||	 min: 03, sec: 10
05. shallow neural networks/14. backprop for cost.mp4 	||	 min: 10, sec: 04
05. shallow neural networks/15. convexity.mp4 	||	 min: 10, sec: 34
05. shallow neural networks/16. recap and resources.mp4 	||	 min: 05, sec: 52
==================== Week 05 ====================
Total duration: 03:04:52
=================================================


==================== Week 06 ====================
06. mlp/01. preface.mp4 	||	 min: 01, sec: 27
06. mlp/02. outline.mp4 	||	 min: 02, sec: 04
06. mlp/03. notation and forward pass.mp4 	||	 min: 09, sec: 39
06. mlp/04. parameters.mp4 	||	 min: 11, sec: 42
06. mlp/05. backward pass.mp4 	||	 min: 11, sec: 05
06. mlp/06. backward pass wrap up.mp4 	||	 min: 03, sec: 00
06. mlp/07. decision boundaries.mp4 	||	 min: 09, sec: 59
06. mlp/08. setting the number of neurons and layers.mp4 	||	 min: 09, sec: 08
06. mlp/09. multi class classification.mp4 	||	 min: 03, sec: 01
06. mlp/10. probabilities as the output.mp4 	||	 min: 20, sec: 19
06. mlp/11. softmax intro.mp4 	||	 min: 07, sec: 56
06. mlp/12. side notes.mp4 	||	 min: 04, sec: 11
06. mlp/13. softmax intuition.mp4 	||	 min: 07, sec: 32
06. mlp/14. softmax.mp4 	||	 min: 22, sec: 00
06. mlp/15. softmax regression.mp4 	||	 min: 11, sec: 09
06. mlp/16. nature of softmax.mp4 	||	 min: 02, sec: 08
06. mlp/17. softmax regression vs. logistic regression.mp4 	||	 min: 01, sec: 50
06. mlp/18. multi class cross entropy.mp4 	||	 min: 11, sec: 25
06. mlp/19. properties of multi class cross entropy.mp4 	||	 min: 07, sec: 45
06. mlp/20. extra notes.mp4 	||	 min: 03, sec: 04
06. mlp/21. multi label classification intro.mp4 	||	 min: 01, sec: 07
06. mlp/22. types of classification.mp4 	||	 min: 16, sec: 50
06. mlp/23. loss function for multi label classification.mp4 	||	 min: 09, sec: 36
06. mlp/24. not assigned values.mp4 	||	 min: 06, sec: 04
06. mlp/25. when to use multi label classification.mp4 	||	 min: 08, sec: 39
06. mlp/26. mlp for regression - part one.mp4 	||	 min: 08, sec: 18
06. mlp/27. mlp for regression - part two.mp4 	||	 min: 01, sec: 17
06. mlp/28. gradient checking intro.mp4 	||	 min: 06, sec: 29
06. mlp/29. gradient checking details.mp4 	||	 min: 12, sec: 00
06. mlp/30. some notes for applying gradient checking.mp4 	||	 min: 08, sec: 44
06. mlp/31. recap and extra resources.mp4 	||	 min: 03, sec: 25
==================== Week 06 ====================
Total duration: 04:03:08
=================================================


==================== Week 07 ====================
07. dealing with overfitting/01. preface.mp4 	||	 min: 09, sec: 33
07. dealing with overfitting/02. outline.mp4 	||	 min: 02, sec: 11
07. dealing with overfitting/03. demand for unseen data.mp4 	||	 min: 06, sec: 42
07. dealing with overfitting/04. validation data and generalization.mp4 	||	 min: 09, sec: 33
07. dealing with overfitting/05. bias and variance.mp4 	||	 min: 11, sec: 23
07. dealing with overfitting/06. overlaps of distributions.mp4 	||	 min: 19, sec: 06
07. dealing with overfitting/07. reduciable error and minimum error.mp4 	||	 min: 19, sec: 21
07. dealing with overfitting/08. multiplication rule for the probabilities of the classes.mp4 	||	 min: 03, sec: 00
07. dealing with overfitting/09. bayes error.mp4 	||	 min: 07, sec: 52
07. dealing with overfitting/10. bayes error more examples.mp4 	||	 min: 02, sec: 52
07. dealing with overfitting/11. determining the status of the model intro.mp4 	||	 min: 06, sec: 42
07. dealing with overfitting/12. errors due to bias and variance.mp4 	||	 min: 13, sec: 15
07. dealing with overfitting/13. robust models under different circumstances.mp4 	||	 min: 02, sec: 19
07. dealing with overfitting/14. how to find the status of a model - wrap up.mp4 	||	 min: 03, sec: 09
07. dealing with overfitting/15. solutions for high bias and high variance.mp4 	||	 min: 16, sec: 55
07. dealing with overfitting/16. orthogonalization.mp4 	||	 min: 09, sec: 09
07. dealing with overfitting/17. bias variance trade off in deep learning.mp4 	||	 min: 09, sec: 24
07. dealing with overfitting/18. different partitions.mp4 	||	 min: 08, sec: 29
07. dealing with overfitting/19. how to split dataset.mp4 	||	 min: 11, sec: 14
07. dealing with overfitting/20. imbalanced datasets.mp4 	||	 min: 05, sec: 44
07. dealing with overfitting/21. the distribution of the dataset.mp4 	||	 min: 13, sec: 22
07. dealing with overfitting/22. avoid overfitting intro.mp4 	||	 min: 02, sec: 19
07. dealing with overfitting/23. l2 regularization intro.mp4 	||	 min: 00, sec: 53
07. dealing with overfitting/24. large weights.mp4 	||	 min: 15, sec: 31
07. dealing with overfitting/25. large values interpretation.mp4 	||	 min: 04, sec: 51
07. dealing with overfitting/26. another intuition for l2.mp4 	||	 min: 03, sec: 13
07. dealing with overfitting/27. l2 formula.mp4 	||	 min: 07, sec: 27
07. dealing with overfitting/28. l2 and the graph of the cost.mp4 	||	 min: 03, sec: 32
07. dealing with overfitting/29. tanh and l2.mp4 	||	 min: 08, sec: 00
07. dealing with overfitting/30. bias terms and regularization.mp4 	||	 min: 04, sec: 28
07. dealing with overfitting/31. MLP and l2 and weight decay.mp4 	||	 min: 14, sec: 19
07. dealing with overfitting/32. l2 and weight decay.mp4 	||	 min: 06, sec: 45
07. dealing with overfitting/33. advantages of l2 reg.mp4 	||	 min: 01, sec: 27
07. dealing with overfitting/34. l1 reg.mp4 	||	 min: 10, sec: 39
07. dealing with overfitting/35. dropout mechanism.mp4 	||	 min: 09, sec: 36
07. dealing with overfitting/36. dropout intuitions.mp4 	||	 min: 06, sec: 37
07. dealing with overfitting/37. why dropout works.mp4 	||	 min: 12, sec: 42
07. dealing with overfitting/38. training a model with dropout.mp4 	||	 min: 09, sec: 12
07. dealing with overfitting/39. keep prob.mp4 	||	 min: 11, sec: 08
07. dealing with overfitting/40. some notes about dropout.mp4 	||	 min: 08, sec: 26
07. dealing with overfitting/41. cost function with dropout.mp4 	||	 min: 04, sec: 01
07. dealing with overfitting/42. turning off the dropout.mp4 	||	 min: 03, sec: 58
07. dealing with overfitting/43. inverted dropout.mp4 	||	 min: 15, sec: 03
07. dealing with overfitting/44. dropout at test time.mp4 	||	 min: 01, sec: 47
07. dealing with overfitting/45. early stopping procedure.mp4 	||	 min: 07, sec: 32
07. dealing with overfitting/46. early stopping details.mp4 	||	 min: 13, sec: 24
07. dealing with overfitting/47. drawbacks of early stopping.mp4 	||	 min: 09, sec: 25
07. dealing with overfitting/48. data augmentation.mp4 	||	 min: 15, sec: 39
07. dealing with overfitting/49. data augmentation wrap up.mp4 	||	 min: 05, sec: 35
07. dealing with overfitting/50. recap and resources.mp4 	||	 min: 03, sec: 42
==================== Week 07 ====================
Total duration: 06:52:51
=================================================


==================== Week 08 ====================
08. general challenges/01. preface.mp4 	||	 min: 02, sec: 26
08. general challenges/02. outline.mp4 	||	 min: 05, sec: 12
08. general challenges/03. numerical stability.mp4 	||	 min: 06, sec: 42
08. general challenges/04. saturated gradient.mp4 	||	 min: 07, sec: 53
08. general challenges/05. why normalization is helpful.mp4 	||	 min: 10, sec: 30
08. general challenges/06. cost function for different scales.mp4 	||	 min: 02, sec: 55
08. general challenges/07. advantages of normalizing.mp4 	||	 min: 13, sec: 08
08. general challenges/08. normalizing and standardizing.mp4 	||	 min: 12, sec: 49
08. general challenges/09. standardization example - part one.mp4 	||	 min: 10, sec: 30
08. general challenges/10. standardization example - part two.mp4 	||	 min: 03, sec: 25
08. general challenges/11. some notes about normalization.mp4 	||	 min: 16, sec: 25
08. general challenges/12. tanh as the activation of hidden layers.mp4 	||	 min: 05, sec: 24
08. general challenges/13. vanishing problem.mp4 	||	 min: 08, sec: 42
08. general challenges/14. solutions.mp4 	||	 min: 03, sec: 33
08. general challenges/15. exploding problem.mp4 	||	 min: 04, sec: 02
08. general challenges/16. initialization methods intro.mp4 	||	 min: 08, sec: 27
08. general challenges/17. methods for initialization.mp4 	||	 min: 07, sec: 42
08. general challenges/18. distributions for initialization.mp4 	||	 min: 12, sec: 03
08. general challenges/19. symmetry problem.mp4 	||	 min: 09, sec: 00
08. general challenges/20. symmetry problem details.mp4 	||	 min: 10, sec: 16
08. general challenges/21. random restart intro.mp4 	||	 min: 03, sec: 01
08. general challenges/22. nondeterministic.mp4 	||	 min: 07, sec: 15
08. general challenges/23. random numbers.mp4 	||	 min: 02, sec: 27
08. general challenges/24. stochastic search algorithms.mp4 	||	 min: 17, sec: 17
08. general challenges/25. random weights in neural networks.mp4 	||	 min: 13, sec: 49
08. general challenges/26. gradient checking final notes.mp4 	||	 min: 03, sec: 57
08. general challenges/27. recap and extra resources.mp4 	||	 min: 03, sec: 07
==================== Week 08 ====================
Total duration: 03:32:12
=================================================


==================== Week 09 ====================
09. hyperparameters/01. preface.mp4 	||	 min: 03, sec: 03
09. hyperparameters/02. outline.mp4 	||	 min: 03, sec: 52
09. hyperparameters/03. hyperparameter.mp4 	||	 min: 05, sec: 23
09. hyperparameters/04. types.mp4 	||	 min: 13, sec: 06
09. hyperparameters/05. epoch and iteration.mp4 	||	 min: 17, sec: 46
09. hyperparameters/06. procedure for iteration.mp4 	||	 min: 04, sec: 39
09. hyperparameters/07. mini batch size intro.mp4 	||	 min: 06, sec: 05
09. hyperparameters/08. mini batch details.mp4 	||	 min: 12, sec: 39
09. hyperparameters/09. different approaches to feed data.mp4 	||	 min: 12, sec: 38
09. hyperparameters/10. pseudocode.mp4 	||	 min: 10, sec: 42
09. hyperparameters/11. behavior of different approaches.mp4 	||	 min: 22, sec: 57
09. hyperparameters/12. another interpretation for behaviors.mp4 	||	 min: 02, sec: 56
09. hyperparameters/13. other effects of mini batch size.mp4 	||	 min: 11, sec: 39
09. hyperparameters/14. size of mini batches.mp4 	||	 min: 06, sec: 23
09. hyperparameters/15. learning rate intro.mp4 	||	 min: 13, sec: 45
09. hyperparameters/16. elaborating different cases for learning rate.mp4 	||	 min: 07, sec: 01
09. hyperparameters/17. tuning the learning rate.mp4 	||	 min: 05, sec: 07
09. hyperparameters/18. extra notes for learning rate.mp4 	||	 min: 12, sec: 32
09. hyperparameters/19. example - part one.mp4 	||	 min: 09, sec: 25
09. hyperparameters/20. example - part two.mp4 	||	 min: 03, sec: 20
09. hyperparameters/21. example - part three.mp4 	||	 min: 07, sec: 57
09. hyperparameters/22. learning rate decay.mp4 	||	 min: 10, sec: 12
09. hyperparameters/23. learning rate schedules.mp4 	||	 min: 13, sec: 50
09. hyperparameters/24. cosine annealing - part one.mp4 	||	 min: 23, sec: 51
09. hyperparameters/25. cosine annealing - part two.mp4 	||	 min: 01, sec: 10
09. hyperparameters/26. example.mp4 	||	 min: 01, sec: 31
09. hyperparameters/27. challenges of lr.mp4 	||	 min: 07, sec: 52
09. hyperparameters/28. model hyperparameters intro.mp4 	||	 min: 01, sec: 15
09. hyperparameters/29. capacity of learning.mp4 	||	 min: 26, sec: 36
09. hyperparameters/30. model hyperparameters and overfitting.mp4 	||	 min: 16, sec: 37
09. hyperparameters/31. some guidelines.mp4 	||	 min: 07, sec: 53
09. hyperparameters/32. activation functions intro.mp4 	||	 min: 13, sec: 25
09. hyperparameters/33. types of activation functions.mp4 	||	 min: 03, sec: 37
09. hyperparameters/34. sigmoid.mp4 	||	 min: 04, sec: 12
09. hyperparameters/35. tanh.mp4 	||	 min: 04, sec: 42
09. hyperparameters/36. relu.mp4 	||	 min: 05, sec: 19
09. hyperparameters/37. relu problems.mp4 	||	 min: 11, sec: 14
09. hyperparameters/38. relu problems wrap up.mp4 	||	 min: 03, sec: 12
09. hyperparameters/39. sparsity.mp4 	||	 min: 15, sec: 33
09. hyperparameters/40. softplus.mp4 	||	 min: 04, sec: 13
09. hyperparameters/41. leaky relu.mp4 	||	 min: 04, sec: 49
09. hyperparameters/42. prelu.mp4 	||	 min: 05, sec: 11
09. hyperparameters/43. softmax.mp4 	||	 min: 02, sec: 59
09. hyperparameters/44. identity.mp4 	||	 min: 08, sec: 11
09. hyperparameters/45. recap and extra resources.mp4 	||	 min: 05, sec: 25
==================== Week 09 ====================
Total duration: 06:36:05
=================================================


==================== Week 10 ====================
10. optimization algorithms/01. preface.mp4 	||	 min: 20, sec: 23
10. optimization algorithms/02. outline.mp4 	||	 min: 08, sec: 21
10. optimization algorithms/03. reminder.mp4 	||	 min: 07, sec: 29
10. optimization algorithms/04. error surface.mp4 	||	 min: 18, sec: 34
10. optimization algorithms/05. why iterative approaches.mp4 	||	 min: 05, sec: 17
10. optimization algorithms/06. newton raphson method.mp4 	||	 min: 16, sec: 55
10. optimization algorithms/07. iterative approaches intro.mp4 	||	 min: 13, sec: 08
10. optimization algorithms/08. another interpretation for GD.mp4 	||	 min: 13, sec: 01
10. optimization algorithms/09. newton method.mp4 	||	 min: 25, sec: 13
10. optimization algorithms/10. descending directions.mp4 	||	 min: 07, sec: 38
10. optimization algorithms/11. how to find step size.mp4 	||	 min: 09, sec: 44
10. optimization algorithms/12. sufficient decrease.mp4 	||	 min: 16, sec: 47
10. optimization algorithms/13. interpretation of sufficient decrease.mp4 	||	 min: 04, sec: 33
10. optimization algorithms/14. backtracking.mp4 	||	 min: 09, sec: 37
10. optimization algorithms/15. saddle points and optimization techniques.mp4 	||	 min: 15, sec: 57
10. optimization algorithms/16. why not to use backtracking for deep learning.mp4 	||	 min: 07, sec: 00
10. optimization algorithms/17. poor condition matrices.mp4 	||	 min: 24, sec: 13
10. optimization algorithms/18. poor condition hessian.mp4 	||	 min: 15, sec: 56
10. optimization algorithms/19. momentum intuition.mp4 	||	 min: 19, sec: 45
10. optimization algorithms/20. momentum.mp4 	||	 min: 17, sec: 32
10. optimization algorithms/21. why momentum is better than gd.mp4 	||	 min: 02, sec: 31
10. optimization algorithms/22. nag.mp4 	||	 min: 15, sec: 31
10. optimization algorithms/23. nag depicted.mp4 	||	 min: 10, sec: 32
10. optimization algorithms/24. moving average.mp4 	||	 min: 25, sec: 39
10. optimization algorithms/25. some notes for moving average.mp4 	||	 min: 17, sec: 59
10. optimization algorithms/26. momentum with moving average.mp4 	||	 min: 12, sec: 06
10. optimization algorithms/27. momentum wrap up.mp4 	||	 min: 06, sec: 18
10. optimization algorithms/28. rprop intro.mp4 	||	 min: 11, sec: 18
10. optimization algorithms/29. rporp.mp4 	||	 min: 17, sec: 47
10. optimization algorithms/30. rprop and mini batch.mp4 	||	 min: 11, sec: 17
10. optimization algorithms/31. rmsprop.mp4 	||	 min: 17, sec: 45
10. optimization algorithms/32. adam.mp4 	||	 min: 16, sec: 03
10. optimization algorithms/33. adamw and sgdw intro.mp4 	||	 min: 03, sec: 48
10. optimization algorithms/34. sgdw.mp4 	||	 min: 21, sec: 02
10. optimization algorithms/35. l2 and weight decay are not equivalent.mp4 	||	 min: 10, sec: 14
10. optimization algorithms/36. adam another interpretation.mp4 	||	 min: 10, sec: 01
10. optimization algorithms/37. adamw.mp4 	||	 min: 06, sec: 13
10. optimization algorithms/38. some notes.mp4 	||	 min: 06, sec: 01
10. optimization algorithms/39. plots of adamw and sgdw.mp4 	||	 min: 29, sec: 52
10. optimization algorithms/40. cost function design intro.mp4 	||	 min: 03, sec: 26
10. optimization algorithms/41. closed form intro.mp4 	||	 min: 10, sec: 03
10. optimization algorithms/42. closed form for linear regression.mp4 	||	 min: 09, sec: 16
10. optimization algorithms/43. pseudo inverse.mp4 	||	 min: 09, sec: 10
10. optimization algorithms/44. closed form for logistic regression.mp4 	||	 min: 04, sec: 45
10. optimization algorithms/45. some definitions and interpretations.mp4 	||	 min: 22, sec: 09
10. optimization algorithms/46. penalty function approximation.mp4 	||	 min: 19, sec: 36
10. optimization algorithms/47. penalty function design.mp4 	||	 min: 43, sec: 35
10. optimization algorithms/48. scalarization.mp4 	||	 min: 29, sec: 23
10. optimization algorithms/49. recap and resources.mp4 	||	 min: 12, sec: 10
==================== Week 10 ====================
Total duration: 11:32:57
=================================================


==================== Week 11 ====================
11. covariates/01. preface.mp4 	||	 min: 02, sec: 34
11. covariates/02. distribution matters.mp4 	||	 min: 11, sec: 25
11. covariates/03. covariat shift.mp4 	||	 min: 13, sec: 42
11. covariates/04. batch normalization intuition.mp4 	||	 min: 25, sec: 16
11. covariates/05. batch norm.mp4 	||	 min: 39, sec: 52
11. covariates/06. batch norm more details.mp4 	||	 min: 20, sec: 49
11. covariates/07. batch norm final notes.mp4 	||	 min: 19, sec: 35
11. covariates/08. shuffling.mp4 	||	 min: 11, sec: 51
11. covariates/09. recap and resourses.mp4 	||	 min: 01, sec: 24
==================== Week 11 ====================
Total duration: 02:26:32
=================================================


==================== Week 12 ====================
12. hyperparameter tuning/01. preface.mp4 	||	 min: 04, sec: 17
12. hyperparameter tuning/02. outline.mp4 	||	 min: 00, sec: 58
12. hyperparameter tuning/03. priority.mp4 	||	 min: 16, sec: 55
12. hyperparameter tuning/04. random.mp4 	||	 min: 08, sec: 06
12. hyperparameter tuning/05. coarse to fine.mp4 	||	 min: 02, sec: 27
12. hyperparameter tuning/06. appropriate range part one.mp4 	||	 min: 10, sec: 52
12. hyperparameter tuning/07. appropriate range part two.mp4 	||	 min: 08, sec: 37
12. hyperparameter tuning/08. how to look for.mp4 	||	 min: 20, sec: 38
12. hyperparameter tuning/09. recap and extra resources.mp4 	||	 min: 02, sec: 40
==================== Week 12 ====================
Total duration: 01:15:34
=================================================


==================== Week 13 ====================
13. evaluation criterions/01. preface.mp4 	||	 min: 04, sec: 18
13. evaluation criterions/02. outline.mp4 	||	 min: 06, sec: 03
13. evaluation criterions/03. evaluation for classification.mp4 	||	 min: 06, sec: 33
13. evaluation criterions/04. confusion matrix.mp4 	||	 min: 22, sec: 24
13. evaluation criterions/05. precision and recall part one.mp4 	||	 min: 21, sec: 42
13. evaluation criterions/06. precision and recall part two.mp4 	||	 min: 23, sec: 34
13. evaluation criterions/07. precision and recall part three.mp4 	||	 min: 23, sec: 18
13. evaluation criterions/08. classification and thresholding.mp4 	||	 min: 31, sec: 10
13. evaluation criterions/09. precision recall curve.mp4 	||	 min: 22, sec: 21
13. evaluation criterions/10. pr auc.mp4 	||	 min: 05, sec: 31
13. evaluation criterions/11. accuracy.mp4 	||	 min: 21, sec: 37
13. evaluation criterions/12. f one score.mp4 	||	 min: 19, sec: 49
13. evaluation criterions/13. f beta score.mp4 	||	 min: 20, sec: 06
13. evaluation criterions/14. roc curve.mp4 	||	 min: 19, sec: 24
13. evaluation criterions/15. further studies.mp4 	||	 min: 02, sec: 46
13. evaluation criterions/16. mae.mp4 	||	 min: 06, sec: 10
13. evaluation criterions/17. mse.mp4 	||	 min: 08, sec: 35
13. evaluation criterions/18. r squared.mp4 	||	 min: 03, sec: 20
13. evaluation criterions/19. recap and extra resources.mp4 	||	 min: 02, sec: 47
13. evaluation criterions/20. final notes.mp4 	||	 min: 05, sec: 04
==================== Week 13 ====================
Total duration: 04:36:41
=================================================


==================== Week 14 ====================
14. cnn/01. preface.mp4 	||	 min: 12, sec: 18
14. cnn/02. outline.mp4 	||	 min: 10, sec: 29
14. cnn/03. mlp drawback.mp4 	||	 min: 15, sec: 38
14. cnn/04. intuition.mp4 	||	 min: 16, sec: 30
14. cnn/05. structure of data.mp4 	||	 min: 21, sec: 00
14. cnn/06. problems of images and mlp.mp4 	||	 min: 21, sec: 01
14. cnn/07. fully connected to locally connected.mp4 	||	 min: 21, sec: 47
14. cnn/08. locally connected to convolutional.mp4 	||	 min: 28, sec: 09
14. cnn/09. conv nets.mp4 	||	 min: 46, sec: 15
14. cnn/10. padding.mp4 	||	 min: 22, sec: 55
14. cnn/11. stride.mp4 	||	 min: 14, sec: 58
14. cnn/12. different convolutions.mp4 	||	 min: 18, sec: 50
14. cnn/13. convolutional layers part one.mp4 	||	 min: 17, sec: 13
14. cnn/14. convolutional layers part two.mp4 	||	 min: 42, sec: 04
14. cnn/15. convolutional layers part three.mp4 	||	 min: 22, sec: 18
14. cnn/16. stacking layers.mp4 	||	 min: 42, sec: 36
14. cnn/17. pooling.mp4 	||	 min: 50, sec: 58
14. cnn/18. network.mp4 	||	 min: 03, sec: 38
14. cnn/19. hierarchy.mp4 	||	 min: 34, sec: 27
14. cnn/20. feature extractors.mp4 	||	 min: 15, sec: 49
14. cnn/21. visualizing cnns.mp4 	||	 min: 10, sec: 28
14. cnn/22. training a cnn.mp4 	||	 min: 15, sec: 49
14. cnn/23. attack.mp4 	||	 min: 28, sec: 04
14. cnn/24. hyperparameters of cnns.mp4 	||	 min: 23, sec: 59
14. cnn/25. transfer learning for cnns.mp4 	||	 min: 34, sec: 15
14. cnn/26. data augmentation for cnns.mp4 	||	 min: 30, sec: 11
14. cnn/27. applications of cnns.mp4 	||	 min: 10, sec: 24
14. cnn/28. famous image datasets.mp4 	||	 min: 04, sec: 15
14. cnn/29. recap and resources.mp4 	||	 min: 05, sec: 26
==================== Week 14 ====================
Total duration: 10:41:57
=================================================


==================== Week 15 ====================
15. cnn architectures/01. preface.mp4 	||	 min: 23, sec: 56
15. cnn architectures/02. outline.mp4 	||	 min: 03, sec: 34
15. cnn architectures/03. lenet 5.mp4 	||	 min: 15, sec: 33
15. cnn architectures/04. alexnet.mp4 	||	 min: 14, sec: 18
15. cnn architectures/05. vgg.mp4 	||	 min: 10, sec: 09
15. cnn architectures/06. nin.mp4 	||	 min: 27, sec: 18
15. cnn architectures/07. resnet intro.mp4 	||	 min: 18, sec: 43
15. cnn architectures/08. resnet.mp4 	||	 min: 14, sec: 55
15. cnn architectures/09. resnet and more.mp4 	||	 min: 18, sec: 33
15. cnn architectures/10. dense net.mp4 	||	 min: 15, sec: 46
15. cnn architectures/11. google net part one.mp4 	||	 min: 20, sec: 13
15. cnn architectures/12. google net part two.mp4 	||	 min: 16, sec: 24
15. cnn architectures/13. imagenet challenge.mp4 	||	 min: 09, sec: 16
15. cnn architectures/14. image transformations.mp4 	||	 min: 20, sec: 05
15. cnn architectures/15. bilinear interpolation.mp4 	||	 min: 14, sec: 35
15. cnn architectures/16. finding transformation.mp4 	||	 min: 16, sec: 17
15. cnn architectures/17. a big mistake.mp4 	||	 min: 02, sec: 33
15. cnn architectures/18. spatial transformer networks intro.mp4 	||	 min: 29, sec: 13
15. cnn architectures/19. spatial transformer networks.mp4 	||	 min: 44, sec: 38
15. cnn architectures/20. recap and resources.mp4 	||	 min: 04, sec: 01
==================== Week 15 ====================
Total duration: 05:40:08
=================================================


==================== Week 16 ====================
16. word embeddings/01. preface.mp4 	||	 min: 06, sec: 58
16. word embeddings/02. outline.mp4 	||	 min: 01, sec: 52
16. word embeddings/03. word representation.mp4 	||	 min: 19, sec: 20
16. word embeddings/04. challenges.mp4 	||	 min: 13, sec: 22
16. word embeddings/05. word embeddings.mp4 	||	 min: 20, sec: 01
16. word embeddings/06. similarity.mp4 	||	 min: 20, sec: 07
16. word embeddings/07. analogy.mp4 	||	 min: 20, sec: 58
16. word embeddings/08. embedding matrix.mp4 	||	 min: 11, sec: 59
16. word embeddings/09. word2vec part one.mp4 	||	 min: 18, sec: 02
16. word embeddings/10. word2vec part two.mp4 	||	 min: 51, sec: 52
16. word embeddings/11. negative sampling.mp4 	||	 min: 38, sec: 20
16. word embeddings/12. some challenges.mp4 	||	 min: 06, sec: 25
16. word embeddings/13. transfer learning.mp4 	||	 min: 32, sec: 14
16. word embeddings/14. dimensionality reduction.mp4 	||	 min: 09, sec: 52
16. word embeddings/15. self attention.mp4 	||	 min: 37, sec: 00
16. word embeddings/16. debiasing word embeddings.mp4 	||	 min: 22, sec: 57
16. word embeddings/17. recap and resources.mp4 	||	 min: 03, sec: 44
==================== Week 16 ====================
Total duration: 05:35:08
=================================================


==================== Week 17 ====================
17. rnn/01. preface.mp4 	||	 min: 17, sec: 35
17. rnn/02. outline.mp4 	||	 min: 05, sec: 35
17. rnn/03. sequential data.mp4 	||	 min: 29, sec: 18
17. rnn/04. examples of sequence data.mp4 	||	 min: 08, sec: 01
17. rnn/05. rnn part one.mp4 	||	 min: 35, sec: 31
17. rnn/06. rnn part two.mp4 	||	 min: 41, sec: 54
17. rnn/07. different types of rnns.mp4 	||	 min: 13, sec: 17
17. rnn/08. optimization.mp4 	||	 min: 12, sec: 53
17. rnn/09. language model.mp4 	||	 min: 33, sec: 36
17. rnn/10. sampling with lm.mp4 	||	 min: 11, sec: 50
17. rnn/11. vanishing and exploding gradients.mp4 	||	 min: 15, sec: 50
17. rnn/12. gru first part.mp4 	||	 min: 31, sec: 37
17. rnn/13. gru second part.mp4 	||	 min: 22, sec: 24
17. rnn/14. lstm.mp4 	||	 min: 20, sec: 56
17. rnn/15. brnn.mp4 	||	 min: 25, sec: 41
17. rnn/16. deep rnn.mp4 	||	 min: 15, sec: 10
17. rnn/17. seq2seq.mp4 	||	 min: 10, sec: 32
17. rnn/18. rnn hyperparameters.mp4 	||	 min: 12, sec: 03
17. rnn/19. recap and resources.mp4 	||	 min: 05, sec: 40
==================== Week 17 ====================
Total duration: 06:09:28
=================================================


==================== Week 18 ====================
18. autoencoders and gans/01. preface.mp4 	||	 min: 03, sec: 13
18. autoencoders and gans/02. outline.mp4 	||	 min: 01, sec: 08
18. autoencoders and gans/03. autoencoders and compression.mp4 	||	 min: 25, sec: 57
18. autoencoders and gans/04. latent variables.mp4 	||	 min: 06, sec: 59
18. autoencoders and gans/05. details.mp4 	||	 min: 16, sec: 33
18. autoencoders and gans/06. upsampling.mp4 	||	 min: 19, sec: 27
18. autoencoders and gans/07. convolutional layers.mp4 	||	 min: 09, sec: 46
18. autoencoders and gans/08. discriminative and generative models.mp4 	||	 min: 21, sec: 16
18. autoencoders and gans/09. gan intro.mp4 	||	 min: 21, sec: 00
18. autoencoders and gans/10. discriminator.mp4 	||	 min: 09, sec: 00
18. autoencoders and gans/11. generator.mp4 	||	 min: 18, sec: 09
18. autoencoders and gans/12. training of a gan.mp4 	||	 min: 10, sec: 24
18. autoencoders and gans/13. cost functions part one.mp4 	||	 min: 31, sec: 50
18. autoencoders and gans/14. kl divergence.mp4 	||	 min: 09, sec: 36
18. autoencoders and gans/15. proof.mp4 	||	 min: 26, sec: 01
18. autoencoders and gans/16. details of the generative cost.mp4 	||	 min: 06, sec: 30
18. autoencoders and gans/17. mode collapse.mp4 	||	 min: 10, sec: 10
18. autoencoders and gans/18. wgan.mp4 	||	 min: 22, sec: 51
18. autoencoders and gans/19. recap and extra resources.mp4 	||	 min: 06, sec: 29
==================== Week 18 ====================
Total duration: 04:36:26
=================================================


==================== Week 19 ====================
19. managing the challenges vol. 1/01. preface.mp4 	||	 min: 09, sec: 36
19. managing the challenges vol. 1/02. outline.mp4 	||	 min: 07, sec: 52
19. managing the challenges vol. 1/03. technical strategies.mp4 	||	 min: 19, sec: 57
19. managing the challenges vol. 1/04. single number evaluation metric.mp4 	||	 min: 14, sec: 04
19. managing the challenges vol. 1/05. optimizing and satisficing metrics.mp4 	||	 min: 11, sec: 27
19. managing the challenges vol. 1/06. dataset distribution.mp4 	||	 min: 11, sec: 39
19. managing the challenges vol. 1/07. size of data partitions.mp4 	||	 min: 09, sec: 46
19. managing the challenges vol. 1/08. small datasets.mp4 	||	 min: 31, sec: 05
19. managing the challenges vol. 1/09. labeled data.mp4 	||	 min: 35, sec: 29
19. managing the challenges vol. 1/10. when to change dev test sets and metrics.mp4 	||	 min: 37, sec: 12
19. managing the challenges vol. 1/11. all together.mp4 	||	 min: 08, sec: 11
19. managing the challenges vol. 1/12. human level performance.mp4 	||	 min: 57, sec: 13
19. managing the challenges vol. 1/13. bias variance.mp4 	||	 min: 45, sec: 15
19. managing the challenges vol. 1/14. improve the performance.mp4 	||	 min: 04, sec: 53
19. managing the challenges vol. 1/15. error analysis.mp4 	||	 min: 48, sec: 20
19. managing the challenges vol. 1/16. recap.mp4 	||	 min: 01, sec: 41
==================== Week 19 ====================
Total duration: 05:53:45
=================================================


==================== Week 20 ====================
20. managing the challenges vol. 2/01. preface.mp4 	||	 min: 03, sec: 41
20. managing the challenges vol. 2/02. outline.mp4 	||	 min: 05, sec: 31
20. managing the challenges vol. 2/03. mismatched data part one.mp4 	||	 min: 42, sec: 53
20. managing the challenges vol. 2/04. mismatched data part two.mp4 	||	 min: 27, sec: 56
20. managing the challenges vol. 2/05. end to end deep learning.mp4 	||	 min: 49, sec: 03
20. managing the challenges vol. 2/06. choosing pipeline components.mp4 	||	 min: 17, sec: 44
20. managing the challenges vol. 2/07. directly learning rich outputs.mp4 	||	 min: 07, sec: 47
20. managing the challenges vol. 2/08. error analysis by parts.mp4 	||	 min: 07, sec: 44
20. managing the challenges vol. 2/09. attributing error to one part.mp4 	||	 min: 08, sec: 49
20. managing the challenges vol. 2/10. general case of error attribution.mp4 	||	 min: 09, sec: 18
20. managing the challenges vol. 2/11. error analysis by parts and comparison to human level performance.mp4 	||	 min: 07, sec: 55
20. managing the challenges vol. 2/12. spotting a flawed ml pipeline.mp4 	||	 min: 08, sec: 59
20. managing the challenges vol. 2/13. recap and resources.mp4 	||	 min: 01, sec: 12
==================== Week 20 ====================
Total duration: 03:18:37
=================================================


==================== Course (1 to 20) ====================
Total duration: 103:47:41
Total size: 26G, 863M, 990K
==========================================================


==================== Total ====================
Total duration: 103:47:41
Total size: 26G, 863M, 990K
===============================================