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Daia A. Romania
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Recent activity : online
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Daia A.
N/A
Romania
Recent activity : online
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Freelancer skills
About freelancer
💪 ★Natural Language Processing , Image classification , topic modelling , sentiment analysis , ★genetic algos's , R , Pyhon , sklearn , tensorlflow , keras exprt and lots more ✓
💪 ★Machine learning experience in analyzing data at scale and creating insights that drive value. ✓
💪 ★ PhD in progress ✓
💪 ★Designed, developed and deployed deep learning systems in production on scalable cloud deployments or at the edge. ✓
💪 ★Expert in the whole pipeline from concept to design of the deep learning algorithms ✓
Education
Mathrematician and Programmer at University Of Bucharest - 2005 to 2008
Phd Candidate at Faculty of Cybernetics, Statistics and Economic Informatics - 2018 to 2021
High school- Math and Computer Science at Henri Coanda High School - 2001 to 2005
Work experiences
Machine Learning Researcher at Upwork inc - 2013 to 2021
Mathematician at Hidroelectrica - 2009 to 2013
Programmer at Icemenerg - 2008 to 2009
Substitute teacher at High School Edmond Nicolau - 2006 to 2007
Intern at Star Storage - 2006 to 2006
Portfolios
State of art solutions to detect on a huge data set outliers which represented bank fraud
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Sentiments extractions in order to improve marketing campaings
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Having a data set with gene expressions from colon \ncancer, we aimed to construct a new feature to \nimprove machine learning\n classification task.\nKey words:\nfeature extraction, gene expression, machine le\narning, logistic regres\nsion, Dan Barbilian, \ndata science, colon cancer, genes, associated genes
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EXperimental results using Self Organizing Maps (SOM) also called Kohonen neural \nnetworks for analyzing systolic hypertension using clinical and bio clinicalparameters. \nWe have conducted experiments using Sompy library available as open source in Python on 900 diabetes patients and concluded this particular type of artificial neural networks could learn patterns regarding correlation of systolic hypertension with different other clinical or bioclincal values, which we consider surprising due to \nthe fact self-organizing detects domain knowledge in data. \n
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Top skills
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