A brief Introduction here. (Wrote a blog about it last year, but do not think it is detailed.) This blog is learning notes from this video (English slides but Chinese speaker). First a quick introduction on SVM, then the magic of how to solve max/min values. Also, you could find Kernel SVM.
Category Archives: Algorithm
Two sample problem(1): Parzen Windows, Maximum Mean Discrepancy
There is a nice tutorial from Alex. I expanded the math part to show you more details. I used latex then posted screenshots.
NLP 05: From Word2vec to Doc2vec: a simple example with Gensim
Introduction First introduced by Mikolov 1 in 2013, the word2vec is to learn distributed representations (word embeddings) when applying neural network. It is based on the distributed hypothesis that words occur in similar contexts (neighboring words) tend to have similar meanings. Two models here: cbow ( continuous bag of words) where we use aContinue reading “NLP 05: From Word2vec to Doc2vec: a simple example with Gensim”
NLP 04: Log-Linear Models for Tagging Task (Python)
We will focus on POS tagging in this blog. Notations While HMM gives us a joint probability on tags and words: . Tags t and words w are one-to-one mapping, so in the series, they share the same length.
TensorFlow 04 : Implement a LeNet-5-like NN to classify notMNIST Images
The blog is a solution of Udacity DL Assignment 4, using a CNN to classify notMNIST images. Visit here to get a full version of my codes.
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.
Parallel Graph Coloring Algorithms and an Implementation of Jones-Plassmann
Graph Coloring Algorithms
Parallel Gibbs Sampling and Neural Networks
Parallel in Variables (Vertexes): General huge, undirected graph: each vertex is a variable (parallel sampling on a high dimension).
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)
IreneLi 