deepanjan90 Goto Github PK

allstate_claims_serverity

a kaggle competition

aws-sdk-java

The official AWS SDK for Java.

blog-site

Blog Website from scratch using Mongo/Mongoengine/Flask

bluewater-minsky-rmg

buildingmachinelearningsystemswithpython

Source Code for the book Building Machine Learning Systems with Python

czar.tech.github.io

everything_java

google_next_24

hello-world

Hello world repository, first hands on github

homebrew-dj

kube-oidc-dj

machine-learning

Machine Learning Projects using R, Mahout, Python and NLP

medicrelief

models

Models built with TensorFlow

nasa-bluewater-psdns-benchmark-minsky

nasa-nas-benchmark-minsky

parallelising_nlp_tasks_using_map-reduceparadigm

Natural Language Processing(NLP) refers to the applications that deal with natural language in a way or other. The proposed system tries to parallelise NLP tasks using map-reduce paradigm. MapReduce is a framework for per- forming data-intensive computations in parallel on commodity computers. The main NLP tasks that the proposed system performs are POS tagging, Stemming, Stopword Removal, Named-entity recognition and Sentimental analysis. The MapReduce algorithm is implemented in Hadoop which is an open source framework for writing and running distributed applications that process large amounts of data. If tasks like POS tagging and Stemming are done sequentially, it may take a lot of time. This is where Hadoop is relevant. Hadoop scales up linearly to handle larger data sets by adding more nodes to the cluster. It also allows users to quickly write efficient parallel code. Keywords: Map-Reduce, NLP, Hadoop, POS Tagging, Stemming, Stopword Removal, Named-Entity Recognition, Sentimental Analysis

picocli-test

producerconsumer

3 Producer 1 Consumer without using semaphore.h

something_python

Anything coded in python

streamlit-stuff

tsa-body-scan-analysis