12:04

10 3 Model Selection and Train Validation Test Sets 12 min)

11:21

10 5 Regularization and Bias Variance 11 min)

07:43

10 4 Diagnosing Bias vs Variance 8 min)

07:36

10 2 Evaluating a Hypothesis 8 min)

05:51

10 1 Deciding What to Try Next 6 min)

13:24

9 7 Putting It Together 14 min)

06:31

9 8 Autonomous Driving 7 min)

11:38

9 5 Gradient Checking 12 min)

06:52

9 6 Random Initialization 7 min)

07:48

9 4 Implementation Note Unrolling Parameters 8 min)

12:45

9 3 Backpropagation Intuition 13 min)

12:00

9 2 Backpropagation Algorithm 12 min)

06:44

9 1 Cost Function 7 min)

10:21

8 6 Examples and Intuitions II 10 min)

07:16

8 5 Examples and Intuitions I 7 min)

03:52

8 7 Multiclass Classification 4 min)

11:47

8 4 Model Representation II 12 min)

12:02

8 3 Model Representation I 12 min)

07:48

8 2 Neurons and the Brain 8 min)

09:36

8 1 Non linear Hypotheses 10 min)

08:34

7 4 Regularized Logistic Regression 9 min)

10:41

7 3 Regularized Linear Regression 11 min)

10:11

7 2 Cost Function 10 min)

09:43

7 1 The Problem of Overfitting 10 min)

14:07

6 6 Advanced Optimization 14 min)

10:15

6 5 Simplified Cost Function and Gradient Descent 10 min)

14:50

6 3 Decision Boundary 15 min)

11:26

6 4 Cost Function 11 min)

07:25

6 2 Hypothesis Representation 7 min)

08:09

6 1 Classification 8 min)

13:49

5 6 Vectorization 14 min)

03:34

5 7 Working on and Submitting Programming Exercises 4 min)

12:56

5 5 Control Statements for while if statements 13 min)

09:39

5 4 Plotting Data 10 min)

13:15

5 3 Computing on Data 13 min)

16:08

5 2 Moving Data Around 16 min)

14:00

5 1 Basic Operations 14 min)

06:16

6 7 Multiclass Classification One vs all 6 min)