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Project for "Vaibhav"
“He is familiar with Machine learning and python. Very good job. very supportive. Good communication”Fu L. 3 weeks ago
Text classification using neural networks concept writeup
“He is a real scientist.”Jonah K. 1 month ago
Personal Project for Visionion
“He has the deep learning expertise.”karacm 1 month ago
Project for Visionion -- 2
“Perfect work again, really it's my good luck working with you sir. Realy thanks so much, perfect job with high-quality results. Thanks for your advice and your patience. Recommended”deeperdragon 2 months ago
Project for Visionion
“another time I work with him and he is very professional and complete the project with success and total satisfaction. very recommended”deeperdragon 2 months ago
Project for Vaibhavkumar P. -- 2
“He is the best in this field of deep learning and machine learning, he is professional and very intelligent, he helps me a lot in this project. I highly recommend him and I will work with him again. He is also very kind and likes challenges. [login to view URL]”deeperdragon 2 months ago
Machine Learning Research EngineerJan 2018 - Apr 2018 (3 months)
I worked there as a machine learning research project named figgymation. My task was to develop a deep learning algorithm to generate an animation from a sketch.
A generative adversarial network for tone mapping HDR images
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.
Convolutional neural network with transfer learning for rice type classification
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.
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