Winter forming Algorithms to mimic human brain.

Winter Internship Report

Designing a Part Of Speech Tagger

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By

Sudhanshu Srivastava

1506041

NIT Patna, Bihar

Under the Guidance of

Dr. A. K. Singh

 

 

 

 

 

 

 

 

 

 

 

 

 

Department of Computer Science &
Engineering

INDIAN INSTITUTE OF TECHNOLOGY
(BANARAS HINDU UNIVERSITY)

VARANASI – 221005

Artificial Intelligence

It could be taken as the superset of
machine learning which itself is a superset of deep learning. On a frank scale,
it could be said as the Technology which gives a machine human like
computational approach.

 

Natural
language processing

A branch of Artificial Intelligence
which deals with the way of  communicating
with a machine/intelligent system with any natural  language like  English or Hindi.

 

Machine
learning

Giving a computer the ability to learn
without being explicitly programmed on that very interest. Basically, training
a system on the past so that it could predict the output of present/future.

 

It has two Sub branches –

o  
Supervised
Learning

o  
Unsupervised
learning

 

Machine learning is the superset of
Deep learning.

 

 

Deep
learning

The machines generate their features
by themselves, basically forming Algorithms to mimic human brain.

It is implemented through neural
networks which has a basic unit called perceptron which is the functional unit
of the neural networks.

The basic Structure of a perceptron. At
first the weights are randomly assigned to the inputs.

Back
propagation method

Compares the output with the given
output and changes the weight correspondingly.

Multiple neural network with several
hidden layers constitute of deep network

Feed
forward networks

Networks that are not cyclic in
nature, i.e. the outputs are independent of each other.

 

Convolutional
neural network

Here, a neuron in a layer is only
connected to a small region of the layer before it. It’s a feed forward neural
network inspired from the visual cortex.

Recurrent
neural networks

The neural network in which the
present output depends on the previous outputs (Could be understood as an
analogy to Dynamic programming).

 

 

 

 

Basic structure of a RNN

There are some limitations with RNN

Vanishing
gradient problem

When the change in weight is very very
small i.e(

x

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