Recurrent Neural Network(RNN Part-1)

 

Sequential Data

In mathematics, a sequence is an enumerated collection of objects in which repetitions are allowed and order matters. Like a set, it contains members (also called elements, or terms). The number of elements (possibly infinite) is called the length of the sequence. Unlike a set, the same elements can appear multiple times at different positions in a sequence, and unlike a set, the order does matter.

Sequential Data - Representation

  • Every element of sequence can be a D dimensional vector as well 
  • In that case a sample is a T x D dimension array  
  • For scalar elements a sample is a T x 1 dimension array 
  • Every sample for a sequential data have 2 dimensions (T and D)

Sequence Modeling Problem

  • Input is a sequence of length T  
  • Output is another sequence of length Q 
  • T = Q - POS Tagging 
  • T > Q - Speech to Text 
  • T < Q - Translation , this can be T >= Q as well 

MLP Limitations

For any layer with input x, trainable weight W and Bias b we can write the Transformation equation (without non-linearity) as

y = W.x + b

  • Some data are inherently SEQUENTIAL in nature and MLP is not a natural choice 
  • We can model some data as a SEQUENCE for better representation

Why MLP is not Suitable? 

  • How can we feed 2D input (T x D) in MLP that accepts a 1D input ? 
  • We can FLATTEN the 2D input to make it 1D (row major / column major ordering) 
  • This destroy “Temporal Relation” between sequential elements 
  • MLP considers all elements of input to be “Temporally Independent” to others which is not True

RNN can solve these problems in one architecture.

How RNN Works? 

Autoregressive Model

Simple linear regression and AR models differ by the fact that Y is dependent on X and previous values for Y. In time series modeling, a Nonlinear Autoregressive Exogenous model (NARX) is a nonlinear autoregressive model which has exogenous inputs. 
This means that the model relates the current value of a time series to 

  • Past values of the same series 
  • Current and past values of the driving (exogenous) series — that is, of the externally determined series that influences the series of interest.


Comments

Post a Comment

Popular posts from this blog

Supervised Learning(Part-5)

Image
  Decision Trees (Continued) Attribute Selection Measures If the dataset consists of N attributes then deciding which attribute to place at the root or at different levels of the tree as internal nodes is a complicated step. By just randomly selecting any node to be the root can’t solve the issue. If we follow a random approach, it may give us bad results with low accuracy. For solving this attribute selection problem, researchers worked and devised some solutions. They suggested using some criteria like : Entropy, Information gain, Gini index, Gain Ratio, Reduction in Variance Chi-Square These criteria will calculate values for every attribute. The values are sorted, and attributes are placed in the tree by following the order i.e, the attribute with a high value(in case of information gain) is placed at the root. While using Information Gain as a criterion, we assume attributes to be categorical, and for the Gini index, attributes are assumed to be continuous. Entropy Entropy is a me
Read more

Supervised Learning(Part-2)

Image
  Support Vector Machine A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples.  In two dimensional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side. Suppose given plot of two label classes on graph as shown in image. You might have come up with something similar to following image (image B). It fairly separates the two classes. Any point that is left of line falls into black circle class and on right falls into blue square   In real world application, finding perfect class for millions of training data set takes lot of time. This is called regularization parameter.  Two terms regularization parameter and gamma are tuning parameters in SVM classifier. Varying those we can achieve considerable non linear classification line with more accuracy in reasonable amount of
Read more

Text Analysis (Part - 4)

Image
  The article covers a very important concepts of Text Analysis from Bayesian's i.e the  Latent Dirichlet Allocation  which is the part of topic modelling . What is Topic Modelling? Topic modelling is a type of statistical modelling for discovering the abstract “topics” that occur in a collection of documents. This can be useful for search engines, customer service automation, and any other instance where knowing the topics of documents is important . Thus, Topic Models , in a nutshell, are a type of statistical language models used for uncovering hidden structure in a collection of texts. There are multiple methods of for topic modelling and one of them includes Latent Dirichlet Allocation (LDA) . Latent Dirichlet Allocation Latent Dirichlet Allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic. It builds a topic per document model and words per topic model, modelled as Dirichlet distributions. Basic Idea LDA is a  genera
Read more