Notes
Rough notes
- overview of callbacks
- partial python function docs
Variance
how far a data point is from the mean
Different ways to measure variance
variance - how far is the value from the mean, square that to get rid of negatives
np.mean(np.square(x-mean))
standard deviation (std) - more sensitive to outlier because you are squaring values `np.sqrt(np.mean(np.square(x-mean)))
mean absolute deviation - instead of multiplying to get rid of the negatives we just take there absolute values,
np.mean(np.absolute(x-mean))
covarince - tells us if variables are positively or neagative correlated there is no mean atributed
to the size of the result. np.mean((x - xmean)*(y-ymean))
to solve this enter correlation
correlation - results in a number lies in a range from -1 to 1. 1 indicates posiitve correlation, -1 a negative correlation
np.mean((x - xmean)*(y-ymean))/ np.sqrt(np.mean(np.square(x-mean))* np.mean(np.square(y-ymean)))
Loss
Softmax vs binary loss
softmax assumes there is always a class present in the image and ?? will always look to maximise a prediction?? .This becomes and issue when data contains muliple classes or no classes.
muliti-label binary classification is the likelehood that data contains a class and is independent from the other class logits. You can have two classes with the same likilehood in a sample.
Callbacks and hooks
using callbacks and hooks to inspect model activations including the
Nomralisation in the model
Why do we need to do it?
Types:
Jermeys running-batch-norm