Deep Learning on Coursera(III)

Posted by Kaiyuan Chen on September 16, 2017

How to structure projects


chain of assumption: training set well -> dev set -> test set -> perform well in real world


maximize Accuracy subject to running time < XX ms then accuracy is optimizing metric and running time is satisfying metric

Dev/Test sets

dev set is development set use dev set for optimization and close and close to target then use test set is test

Human level performance

Bayes optimal error: best possible error for function x -> y

Error Analysis

ceiling of optimization: the max of total improvement on have a table about every image that misclassified and then add comments on it

deep learning algorithm is robust to random errors (reasonably) but not systematic error

training-dev set: same distribution as training set, but not used for training then we can see a difference between variance problem and mismatch problem

training error 1% t - d error 10% then not generalize not well and vise versa, it is data mismatch problem(high diff btw training-dev and dev error) so we can get a error type chart

HUMAN LEVEL **avoidable bias ** TRAINING ERROR **variance ** TRAINING_DEV SET ERROR **data mismatch ** DEV ERROR **degree of overfitting to dev set ** TEST ERROR

Transfer learning

instead of retraining from beginning, because there is much less data available for specific case just add layers of neurons after the output layer

Assumption: Task A and B have same input x task A has much more data than B (to transfer from A to B)

multi-task learning

multi-output neural network unlike softmax regression: set single label to single example this assign different labels training four labels is faster than training 4 nns you can still train with some labels missing just sum over available values

Assumption: training a set of tasks that could benefit from having shared lower-level features amount of data is similar can train a big enough neural network to do well

End-to-end learning

Instead of having a series of layers (like transferring to one format then another)