Word vector

By -
We present a word vector using in deep learning. A definition and a co-occurence matrix of word vector are briefly introduced.

word vector

Comments

Post a Comment

Popular posts from this blog

Feature scaling

By -
Image
1. What is feature scaling?  Feature scaling is a method used to normalize all independent values of our data. We also call it as data normalization and is generally performed before running machine learning algorithms. 2. Why do we need to use feature scaling?  In practice the range of raw data is very wide and hence the object functions will not work properly (means that it will stuck at local optimum), or they are time-consuming without normalization. For example:  K-Means, might give you totally different solutions depending on the preprocessing methods that you used. This is because an affine transformation implies a change in the metric space: the Euclidean distance between two samples will be different after that transformation. When we apply gradient descent, feature scaling also helps it to converge much faster that without normalized data.  With and without feature scaling in gradient descent 3. Methods in feature scaling? Rescaling The simp...
Read more

Manifold Learning [unfinished]

By -
Image
I. What is manifold learning Dimensionality Reduction The accuracy of the training algorithms is directly proportional to the amount of data we have. However, managing a large number of features is usually a burden to our algorithm.  Some of these features may be irrelevant, so it's important to make sure that the final model doesn't get affected by this.  What is Dimensionality?  Dimensionality refers to the minimum number of coordinates needed to specify any point within a space or an object. Why do we need Dimensionality Reduction? If you keep the dimensionality high, it will be nice and unique but it may not be easy to analyse because of complexity involved. Apart from simplifying data, visualization is also an interesting and challenging application. Linear Dimensionality Reduction Fig 1 : PCA illustration (source: http://evelinag.com/blog ) Principle Component Analysis  Given a data set, PCA finds the directions along which the d...
Read more

Parameter estimation in text modeling [unfinished]

By -
Image
In this post, we would like to present some parameter estimation methods common with discrete probability distribution, which is very popular in text modeling. Then we explain the model of Latent Dirichlet Allocation (LDA) in detail. I. Introduction There are two inference problems in parameter estimation: (1) how to estimate values for a set of distribution parameters theta that can best explain a set of observation data. (2) calculate the probability of a new observation given by previous observation. We introduce Bayes' rule to solve these problem above. Bayes' rule is defined as: and may be called:  We will introduce maximum likelihood, a posteriori and Bayesian estimation, central concepts like conjugate distributions and Bayesian networks to tackle two problems. II. Parameter estimation methods 1. Maximum likelihood estimation Maximum likelihood (ML) tries to find the parameters that maximize the likelihood The common way to obtain the parameter es...
Read more