some cmd

This document may be a little massi.. this is just served as an reference as I am current doing some experiment on caffe and wants to mark down something. I may make it tidy when I think it is time to :)

official caffe tour

• nets, layers and blobs
• forward/backwards
• loss
• solver
• layer catalogue
• interfaces
• data
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linear regression model can be interprete from probabilistic view of point
you will find it ‘magical’ that least square appear in the same form as maximum likelihood estimation.
Also notice that ridge regression can also be approached through Bernoulli distribution.
I went through a hard time struggling about the term probability likehood
and their relation.. This is severed as a reference. I think I may need to make it cleanner….after I give a summary of GLM and exponential distribution family.
MLE: maximum likelihood estimate
MAP: maximum a posteriori

reference book I am currently reading…

1. PRML
2. machine learning, a probalistic approach: murphy
3. the elements of statistical learning:HTF
4. 
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