Was working on my research with sklearn, but realized that choosing the right evaluation metrics was always a problem to me. If someone asks me ,”does your model performs well?” The first thing in my mind is “accuracy”. Besides the accuracy, there are a lot, depending on your own problem. Continue reading
Statitics
NLP 01: Language Modeling Problems
PGM 02: Lots of Markov Family members: MC, PMN, CRF…
Markov Chain
Markov Process is a kind of random process. The main idea is given the current state of the system, its future state does not depend on its past states.
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Deep Learning 10: Sequence Modeling
Learning notes for Lecture 7 Modeling sequences: A brief overview. by Geoffrey Hinton [1]
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Deep Learning 09: Small Tricks(2)
Let’s try some ways to speedup our learning!
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Lucky or not: Monte Carlo Method
AlphaGo!
http://c.brightcove.com/services/viewer/federated_f9?isVid=1&isUI=1
When you play any games, probably you have strategies or experiences. But you could not deny that some times you need luck, which data scientists would say a “random choice”. Monte Carlo Method provides only an approximate optimizer, thus giving you the luck to win a game.
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Gibbs Sampling: about Parallelization
About BN
Belief Network, or directed acyclic graphical model (DAG).
When BN is huge:
Exact Inference(variable elimination)
Stochastic Inference(MCMC)
Loopy BP: an easy implementation on Pregel Model
Pregel: Message Passing.
Focus on the process, no matter each vertex computation.
Steps [1]:
(this part was referenced from a blog)
MCMC:Gibbs Sampling
In the Important Sampling, all the samples are independent. But in MCMC, samples are dependent.
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