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$20 USD / hora
Bandera de PAKISTAN
faisalabad, pakistan
$20 USD / hora
Aquí son las 3:06 p. m.
Se unió el octubre 16, 2016
2 recomendaciones

Rohan T.

@rohaan2

 4,9 (22 comentarios)
5,4
5,4
84%
84%
$20 USD / hora
Bandera de PAKISTAN
faisalabad, pakistan
$20 USD / hora
96 %
Trabajos finalizados
75 %
Dentro del presupuesto
66 %
A tiempo
21 %
Tasa de recontratación

Expert AI, Machine Learning and Web Developer

I'm Data Scientist and Computer Scientist, can provide you best services in these areas: 1) Machine Learning (Deep Learning) using Python Pytorch,scikit learn, Keras, Tensorflow . 2) GAN, Auto-Encoders, Deep CNN networks for image Generation, Classification. 3) NLP, Speech recognition, Classification. 4) Time Series Analysis and prediction using regression. 5) Web design and development using JavaScript, Node.js, React.js ,PHP, MySQL, MongoDB, HTML5, CSS and Wordpress. 6) Image Processing applications using OpenCV or Matlab 7) Software Engineering (UML Diagrams ) 8) Database Management ( Database Schema ) 9) Matlab Problem Solving 10) Academic and Research writing My Motto: To provide quality services to my reliable clients.
Freelancer Machine Learning Experts Pakistan
Ver todo Machine Learning Experts en Pakistan

Contacta Rohan T. sobre tu trabajo

Inicia sesión para comentar cualquier detalle por chat.

Portafolio

9656524
In this project, I have done scraping of all the images from any E-commerce site using beautiful soup library in python. Upon scraping all the images and store into a folder we can query an image and find N numbers of similar images from the directory using ML-based techniques.
Scrap Images from a website & get same images as query image
9043790
Perform detection and classification of vehicle color using a powerful deep neural network. The results were quite impressive with an accuracy of 96 percent.
CNN Based Multi-Class Classification of Vehicle color
Perform detection and classification of vehicle color using a powerful deep neural network. The results were quite impressive with an accuracy of 96 percent.
CNN Based Multi-Class Classification of Vehicle color
Perform detection and classification of vehicle color using a powerful deep neural network. The results were quite impressive with an accuracy of 96 percent.
CNN Based Multi-Class Classification of Vehicle color
9043781
In recent years, with the rapid development of deep learning theory and hardware, deep learning is more and more widely used in the field of medical image processing and has greatly improved the performance of traditional methods. Medical image segmentation still is an important research field in the area of computer vision and machine learning. In deep learning, specially Generative adversarial network (GAN) has revolutionized the computer vision domain with its unique architecture, that the model itself does evaluation using discriminator. The basic structure of GAN is that there is a deep learning-based generator that generates the output and there is a discriminator that compares the generated output with real image and computes the loss of the model. The U-Net-based GAN has the tendency to effectively and efficiently do segmentation from complex structures like medical imaging. The proposed method outperforms and achieves high-quality segmented images.
A Deep Learning based Approach Medical Image Segmentation
In recent years, with the rapid development of deep learning theory and hardware, deep learning is more and more widely used in the field of medical image processing and has greatly improved the performance of traditional methods. Medical image segmentation still is an important research field in the area of computer vision and machine learning. In deep learning, specially Generative adversarial network (GAN) has revolutionized the computer vision domain with its unique architecture, that the model itself does evaluation using discriminator. The basic structure of GAN is that there is a deep learning-based generator that generates the output and there is a discriminator that compares the generated output with real image and computes the loss of the model. The U-Net-based GAN has the tendency to effectively and efficiently do segmentation from complex structures like medical imaging. The proposed method outperforms and achieves high-quality segmented images.
A Deep Learning based Approach Medical Image Segmentation
In recent years, with the rapid development of deep learning theory and hardware, deep learning is more and more widely used in the field of medical image processing and has greatly improved the performance of traditional methods. Medical image segmentation still is an important research field in the area of computer vision and machine learning. In deep learning, specially Generative adversarial network (GAN) has revolutionized the computer vision domain with its unique architecture, that the model itself does evaluation using discriminator. The basic structure of GAN is that there is a deep learning-based generator that generates the output and there is a discriminator that compares the generated output with real image and computes the loss of the model. The U-Net-based GAN has the tendency to effectively and efficiently do segmentation from complex structures like medical imaging. The proposed method outperforms and achieves high-quality segmented images.
A Deep Learning based Approach Medical Image Segmentation
8951934
This project used the power of Deep Learning and GAN to do automatic Image inpainting along with Super Resolution of the images to improve the quality of output results.
GAN Based Image Inpainting
8951923
Do Network analysis on the graph datasets available in Stanford Large Network Dataset Collection (https://snap.stanford.edu/data/). Applied the following: • Find the most important nodes (individuals) in the network based on different centrality measures, • Visualise your graph using one of the centrality measures of your choice, • Apply a Community Detection Algorithm to the graph and visualize the communities
Data Visualization and Analysis using Snappy and python
Do Network analysis on the graph datasets available in Stanford Large Network Dataset Collection (https://snap.stanford.edu/data/). Applied the following: • Find the most important nodes (individuals) in the network based on different centrality measures, • Visualise your graph using one of the centrality measures of your choice, • Apply a Community Detection Algorithm to the graph and visualize the communities
Data Visualization and Analysis using Snappy and python
Do Network analysis on the graph datasets available in Stanford Large Network Dataset Collection (https://snap.stanford.edu/data/). Applied the following: • Find the most important nodes (individuals) in the network based on different centrality measures, • Visualise your graph using one of the centrality measures of your choice, • Apply a Community Detection Algorithm to the graph and visualize the communities
Data Visualization and Analysis using Snappy and python
8683434
I have implemented the CVF research paper named "Latent Fingerprint Image Enhancement Based on Progressive Generative Adversarial Network" from scratch. I also generate the noisy data because no public datasets were available. It can efficiently extract the fingerprints from the noisy images. The results are attached in the picture.
Progressive GAN to extract finger prints from Noisy Image
9656524
In this project, I have done scraping of all the images from any E-commerce site using beautiful soup library in python. Upon scraping all the images and store into a folder we can query an image and find N numbers of similar images from the directory using ML-based techniques.
Scrap Images from a website & get same images as query image
9043790
Perform detection and classification of vehicle color using a powerful deep neural network. The results were quite impressive with an accuracy of 96 percent.
CNN Based Multi-Class Classification of Vehicle color
Perform detection and classification of vehicle color using a powerful deep neural network. The results were quite impressive with an accuracy of 96 percent.
CNN Based Multi-Class Classification of Vehicle color
Perform detection and classification of vehicle color using a powerful deep neural network. The results were quite impressive with an accuracy of 96 percent.
CNN Based Multi-Class Classification of Vehicle color
9043781
In recent years, with the rapid development of deep learning theory and hardware, deep learning is more and more widely used in the field of medical image processing and has greatly improved the performance of traditional methods. Medical image segmentation still is an important research field in the area of computer vision and machine learning. In deep learning, specially Generative adversarial network (GAN) has revolutionized the computer vision domain with its unique architecture, that the model itself does evaluation using discriminator. The basic structure of GAN is that there is a deep learning-based generator that generates the output and there is a discriminator that compares the generated output with real image and computes the loss of the model. The U-Net-based GAN has the tendency to effectively and efficiently do segmentation from complex structures like medical imaging. The proposed method outperforms and achieves high-quality segmented images.
A Deep Learning based Approach Medical Image Segmentation
In recent years, with the rapid development of deep learning theory and hardware, deep learning is more and more widely used in the field of medical image processing and has greatly improved the performance of traditional methods. Medical image segmentation still is an important research field in the area of computer vision and machine learning. In deep learning, specially Generative adversarial network (GAN) has revolutionized the computer vision domain with its unique architecture, that the model itself does evaluation using discriminator. The basic structure of GAN is that there is a deep learning-based generator that generates the output and there is a discriminator that compares the generated output with real image and computes the loss of the model. The U-Net-based GAN has the tendency to effectively and efficiently do segmentation from complex structures like medical imaging. The proposed method outperforms and achieves high-quality segmented images.
A Deep Learning based Approach Medical Image Segmentation
In recent years, with the rapid development of deep learning theory and hardware, deep learning is more and more widely used in the field of medical image processing and has greatly improved the performance of traditional methods. Medical image segmentation still is an important research field in the area of computer vision and machine learning. In deep learning, specially Generative adversarial network (GAN) has revolutionized the computer vision domain with its unique architecture, that the model itself does evaluation using discriminator. The basic structure of GAN is that there is a deep learning-based generator that generates the output and there is a discriminator that compares the generated output with real image and computes the loss of the model. The U-Net-based GAN has the tendency to effectively and efficiently do segmentation from complex structures like medical imaging. The proposed method outperforms and achieves high-quality segmented images.
A Deep Learning based Approach Medical Image Segmentation
8951934
This project used the power of Deep Learning and GAN to do automatic Image inpainting along with Super Resolution of the images to improve the quality of output results.
GAN Based Image Inpainting
8951923
Do Network analysis on the graph datasets available in Stanford Large Network Dataset Collection (https://snap.stanford.edu/data/). Applied the following: • Find the most important nodes (individuals) in the network based on different centrality measures, • Visualise your graph using one of the centrality measures of your choice, • Apply a Community Detection Algorithm to the graph and visualize the communities
Data Visualization and Analysis using Snappy and python
Do Network analysis on the graph datasets available in Stanford Large Network Dataset Collection (https://snap.stanford.edu/data/). Applied the following: • Find the most important nodes (individuals) in the network based on different centrality measures, • Visualise your graph using one of the centrality measures of your choice, • Apply a Community Detection Algorithm to the graph and visualize the communities
Data Visualization and Analysis using Snappy and python
Do Network analysis on the graph datasets available in Stanford Large Network Dataset Collection (https://snap.stanford.edu/data/). Applied the following: • Find the most important nodes (individuals) in the network based on different centrality measures, • Visualise your graph using one of the centrality measures of your choice, • Apply a Community Detection Algorithm to the graph and visualize the communities
Data Visualization and Analysis using Snappy and python
8683434
I have implemented the CVF research paper named "Latent Fingerprint Image Enhancement Based on Progressive Generative Adversarial Network" from scratch. I also generate the noisy data because no public datasets were available. It can efficiently extract the fingerprints from the noisy images. The results are attached in the picture.
Progressive GAN to extract finger prints from Noisy Image

Comentarios

Cambios guardados
Mostrando 1 - 5 de 22 reseñas
 Filtrar reseñas por:
5,0
$25,00 USD
Object detection in CNN
Rohan is very helping, understand the problem well and delivers on time. I will hire him again
Python
Machine Learning (ML)
Image Processing
A
Bandera de Ankit J.
@ankitunr
•
hace 3 años
5,0
$240,00 USD
Annotations for MRCNN
Rohan was awesome AGAIN! would definitely hire again!
PHP
Machine Learning (ML)
Neural Networks
Image Processing
+1 más
Avatar del usuario
Bandera de Omar A.
@Ghamdiomar
•
hace 3 años
5,0
$1.000,00 USD
Project for Rohan T.
Rohan worked really hard on challenging deep learning project and had good resilts
PHP
Machine Learning (ML)
Neural Networks
Image Processing
+1 más
C
Bandera de Can A.
@canalhasevam
•
hace 3 años
5,0
$130,00 USD
Yolov5 project simple UI
I loved working with Rohan very professional and experienced would totally hire again!
PHP
Machine Learning (ML)
Neural Networks
Image Processing
+1 más
Avatar del usuario
Bandera de Omar A.
@Ghamdiomar
•
hace 3 años
5,0
$120,00 USD
deep learning project
Great work! Rohan is very smart and always understands and solves the problem immediately. i like working with him, he is a very talented free lancer and delivers high quality deliverables in time and Impressive results and good knowledge about computer vision, deep learning, python. I will here him again
Python
Machine Learning (ML)
Deep Learning
Avatar del usuario
Bandera de Zied S.
@deeperdragon
•
hace 3 años

Experiencia

Web Developer

invictuszone
ene 2017 - feb 2018 (1 año, 1 mes)
Worked as a front-end(HTML) and back-end (PHP) developer

Educación

MS Computer Science

COMSATS Institute of Information Technology, Pakistan 2017 - 2019
(2 años)

BS in Computer Science

National University of Computer and Emerging Sciences, Pakistan 2012 - 2016
(4 años)

Contacta Rohan T. sobre tu trabajo

Inicia sesión para comentar cualquier detalle por chat.

Verificaciones

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Email verificado
Facebook conectado

Principales habilidades

Machine Learning (ML) 18
Image Processing 14
Python 8
JavaScript 4
Computer Vision 3

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