Deciding What to Do Next (Revisited)

2014-05-03 07:03

Deciding What to Do Next (Revisited)

What to do or not to do to increase the learning algorightm

Learning Curves

2014-05-03 06:26

Learning Curves

Curve to check the learning algorithm, whether bias, variance, or both

Regularization and Bias/Variance

2014-05-03 06:03

Regularization and Bias/Variance

Regularization can avoid underfitting/overfitting. But how it does acttually affect the learning algorithms

Diagnosing bias vs. variance

2014-05-03 05:42

Diagnosing bias vs. variance

Most pitfall in machine learning is (bias)underfitting vs (variance)overfitting problems

Model selection and training/validation/test sets

2014-05-03 04:48

Model selection and training/validation/test sets

Choose which features to include

Evaluating a hyphotesis

2014-05-03 04:01

Evaluating a hyphotesis

Choosing the correct parameters whether is underfitting or overfitting

Deciding what to Try Next

2014-05-03 01:22

Deciding what to Try Next

Should be expected by now average expert of machine learning

Autonomous Driving (Examples)

2014-05-02 14:17

Autonomous Driving (Examples)

Neural Networks for Autonomous Driving

Putting it together

2014-05-02 13:45

Putting it together

Implement all pieces together to make overall process for neural networks learning algorithm

Random Initialization

2014-05-02 13:20

Random Initialization

The last pieces in neural networks to be implemented