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Email verificeret.
gandhinagar,
india
Det er i øjeblikket 1:40 PM her
Tilmeldt april 5, 2018
0
Anbefalinger
Vaibhavkumar P.
@vaibhav290797
4,7
4,7
82%
82%
gandhinagar,
india
100 %
Jobs færdiggjort
100 %
Indenfor budgettet
86 %
Til tiden
17 %
Genansættelsesrate
Software Developer
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Viser 1 - 5 ud af 14 anmeldelser
$200,00 USD
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$137,67 USD
J
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$350,00 USD
Python
K
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$75,00 USD
Machine Learning (ML)
Face Recognition
Data Science
Artificial Intelligence
+1 mere
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$100,00 USD
Machine Learning (ML)
Face Recognition
Data Science
Artificial Intelligence
+1 mere
•
Erfaring
SDE-ML
apr. 2020 - feb. 2021 (10 måneder, 1 dag)
Created and deployed a research paper summarizer that takes the full text of the paper and classifies each sentence into various buckets such as research goal, novelty, and limitations. Deployed the app on EC2 and on AWS Lambda.
Worked on PDF parsing pipeline. Gathered multiple available PDF parsers and combined those outputs.
Deployed on AWS.
Deployed figure extraction library on AWS EC2 with health check alarms, auto-scaling, and reporting.
Full-stack Developer
dec. 2018 - mar. 2020 (1 år, 3 måneder)
Created an internal npm library for reporting. Fetched and formatted data from Elastic Search, S3, and DynamoDB.
Worked on a new logic in Express.js (Node.js) when an asset is granted/revoked to sub-account, which reduced the computation as well as database cost 100 fold.
Worked on the dark theme, live training, reporting, and live class on the platform.
SDE-ML
apr. 2018 - dec. 2018 (8 måneder, 2 dage)
Provided services for prototyping, development, and deployment of machine learning algorithms.
Worked on image classification, object detection, and image super-resolution.
Contributed to an IoT project in Python and React, which uses serial communication and picoscope hardware.
Uddannelse
Bachelor's Degree in Computer Science
(4 år)
Publikationer
Convolutional neural network with transfer learning for rice type classification
SPIE digital library
This paper proposes a deep learning based method for classification of rice types.
We propose two methods to classify the rice types. In the first method, we train a deep
convolutional neural network (CNN) using the given segmented rice images. In the second
method, we train a combination of a pretrained VGG16 network and the proposed method,
while using transfer learning in which the weights of a pretrained network are used to
achieve better accuracy.
A generative adversarial network for tone mapping HDR images
Springer
We are proposing a novel generative adversarial network to learn a combination of these tone mapping operators. In order to get pixel level accuracy, we are using residual connections between same-sized network layers. We compare this method with some of the existing tone mapping operators and observe that our method generates images with comparably high TMQI and indeed works on many different types of images.
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