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 “Is your model good enough? Evaluation metrics in Classification and Regression”
Lecture notes from Natural Language Processing (by Michael Collins)
Continue reading “NLP 01: Language Modeling Problems”
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.
Continue reading “PGM 02: Lots of Markov Family members: MC, PMN, CRF…”
Learning notes for Lecture 7 Modeling sequences: A brief overview. by Geoffrey Hinton 
Continue reading “Deep Learning 10: Sequence Modeling”
Let’s try some ways to speedup our learning!
Continue reading “Deep Learning 09: Small Tricks(2)”
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.
Continue reading “Lucky or not: Monte Carlo Method”