class WhoAmI(DataScienceEnthusiast):
''' About myself '''
def __init__(self):
super(WhoAmI, self).__init__()
self.user = "Balamurugan P"
self.edu_status = "Pursuing B.Tech in CSE."
self.edu_institution = "SASTRA University, Thanjavur."
self.passion = [
"Explore Data",
"Helping others to get started",
"Data Science Hackathons",
"Applied Artificial Intelligence"
]
def get_activities():
''' Returns (exp: list) : Extra curricular and community activities. '''
exp = [
"Head - Machine Learning Cluster, Developer Community - SASTRA.",
"Core Team Member, Google Developer Students Club - SASTRA."
]
return exp
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SASTRA University
- Thanjavur, Tamil Nadu, India.
Highlights
- Pro
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Neural-Machine-Translator-App
Neural-Machine-Translator-App PublicThis is a translator app to translate text from English to Italian using neural machine translation (Seq2Seq) model trained with teacher forcing with over 200k+ sentences with an accuracy of 87.57 %.
Python
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Named-Entity-Recognition-using-Tranformers
Named-Entity-Recognition-using-Tranformers PublicIn this repo, I have Fine-tuned BERT using HuggingFace transformers to perform Named Entity Recognition with an overall F1-Score of 0.87.
Jupyter Notebook 1
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English-to-Italian-Neural-Machine-Translator
English-to-Italian-Neural-Machine-Translator PublicThis repo contains training notebook and TFLite conversion notebook of the Neural machine translation model developed using TensorFlow.
Jupyter Notebook
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AI-Chatbot-EduBot-v1-and-v2-SASTRA-using-NLP
AI-Chatbot-EduBot-v1-and-v2-SASTRA-using-NLP PublicThis repo contains an Intent-matching-based chatbot that can reply to the questions regarding a specific educational institute.
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Sentiment-extraction-using-NLP
Sentiment-extraction-using-NLP PublicThis repo contains notebooks for analysis of text data using various EDA techniques and sentiment extraction using different models which use various vector space representations.
Jupyter Notebook 1
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Traffic-Sign-Recognition-using-custom-CNN
Traffic-Sign-Recognition-using-custom-CNN PublicThis repo contains notebooks used for training a CNN-based model with more than 70k images to correctly recognize 43 unique classes of traffic signs with an accuracy >97%.
Jupyter Notebook
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