Posted by in Machine Learning, Statitics, Theory 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.
Posted by in Natural Language Processing, Statitics, Theory Lecture notes from Natural Language Processing (by Michael Collins)
Posted by in Inference Models, Python, Statitics 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.
Posted by in Deep Learning, Machine Learning, Statitics Learning notes for Lecture 7 Modeling sequences: A brief overview. by Geoffrey Hinton [1]
Posted by in Deep Learning, Statitics Let’s try some ways to speedup our learning!
Posted by in Algorithm, Machine Learning, Statitics 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.
About BN Belief Network, or directed acyclic graphical model (DAG). When BN is huge: Exact Inference(variable elimination) Stochastic Inference(MCMC)
