Find Two sample problem (1) here.
We will take a look at RHKS (Reproducing Hilbert Kernel Space ) in this post. You might think of it a very statistical term but it is amazing because of various applications. You will need to refresh your mind for some linear algebra computations. We start with some basic terms and definitions. Continue reading
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. Continue reading
There is a nice tutorial from Alex. I expanded the math part to show you more details. I used latex then posted screenshots. Continue reading
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 a bag of words to predict a target word and skip-gram where we use one word to predict its neighbors. For more, although not highly recommended, have a look at TensorFlow tutorial here. Continue reading
We will focus on POS tagging in this blog.
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.
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.
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.
Graph Coloring Algorithms
Parallel in Variables (Vertexes):
General huge, undirected graph: each vertex is a variable (parallel sampling on a high dimension).