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$10 USD / saat
 INDIA bayrağı
pune, india
$10 USD / saat
Şu anda burada saat 12:53 ÖÖ
Temmuz 15, 2018 tarihinde katıldı
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Saurabh B.

@saurabhbora4

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$10 USD / saat
 INDIA bayrağı
pune, india
$10 USD / saat
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Python, Machine Learning & WordPress Developer

I am a Computer Science undergraduate student currently pursuing engineering from Pune Institute of Computer Technology. My interest lies in Data Science and I aspire to become an AI Expert. ● Proficient in Data Science, Machine Learning, Python, Java, C++, Web Development, SEO and Programming. ● Skilled in leadership, with a strong foundation in math, logic, and cross-platform coding. ● Passionate programmer with clarity of thoughts and matchless quality output. Key accomplishments : ● Finalist among 3000+ teams in the Axis Bank AI Challenge ● Shortlisted as an Intern at NTT DATA, one of the top fortune 100 companies in Tokyo, Japan for a period of 6 weeks ● Qualifier in DST & Texas Instruments India Innovation Challenge Design Contest, IIM Bangalore ● Secured All India Rank (AIR) 17483 in JEE Advanced
Freelancer C Programmers India
C Programmers freelancerlarından India içinden olanlarının tümünü görün.

Saurabh B. ile işiniz hakkında iletişime geçin

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Portföy

5727486
Implemented a machine learning model for Pixel Wise Semantic Segmentation of weed, crop, and background The model is a CNN based encoder-decoder network with residual blocks The input is an RGB image which is stacked with 11 more channels like vegetation indexes, HSV color space, operators on the indices such as the Sobel derivatives, the Laplacian, and the Canny edge detector The 14 channel input helps in the training and generalization of the model due to a small dataset and wide variety of environmental conditions The output is an RGB segmented image of crop, weed, and background Python Implementation - Keras, TensorFlow, OpenCV Used my local GPU Nvidia GTX 960m and Google Colab's free GPU (Tesla K80) for training & testing purpose
Weed Detection in Sugar Beet Plants
Implemented a machine learning model for Pixel Wise Semantic Segmentation of weed, crop, and background The model is a CNN based encoder-decoder network with residual blocks The input is an RGB image which is stacked with 11 more channels like vegetation indexes, HSV color space, operators on the indices such as the Sobel derivatives, the Laplacian, and the Canny edge detector The 14 channel input helps in the training and generalization of the model due to a small dataset and wide variety of environmental conditions The output is an RGB segmented image of crop, weed, and background Python Implementation - Keras, TensorFlow, OpenCV Used my local GPU Nvidia GTX 960m and Google Colab's free GPU (Tesla K80) for training & testing purpose
Weed Detection in Sugar Beet Plants
5727479
A system to detect the objects from a test image set containing color and depth images of multiple occluded objects using the orthographic views. https://youtu.be/16kReY8Vna4
Object Detection using Orthographic views of objects
5727472
Solution Python Implementation • Network Used - Convolutional Siamese Network • Framework - Django Working • User requests to upload the real signature to the database using the POST method. • The server processes the request and the full-size signature is uploaded to the database and is stored inside the customer ID folder of the customer. • For verification, the Customer uploads the required input signature using the POST method. • The server fetches the corresponding real image signature from the database using a customer ID. • The model processes both the inputs and generates a vector corresponding to both of them. • Then the distance between the 2 vectors is calculated and the result is predicted based on the corresponding value. • The predicted result in the form of distance which is compared with the threshold and string output is given to the client and it is displayed on the output screen.
Signature Verification
4361104
Leverage IT for Business & build SaaS products.
WordPress - GOTIB
4361175
Website regarding educational services.
WordPress - Smart Computer India
4361239
Designed an elegant dashboard for Video Calling Application.
Dashboard Design
5727486
Implemented a machine learning model for Pixel Wise Semantic Segmentation of weed, crop, and background The model is a CNN based encoder-decoder network with residual blocks The input is an RGB image which is stacked with 11 more channels like vegetation indexes, HSV color space, operators on the indices such as the Sobel derivatives, the Laplacian, and the Canny edge detector The 14 channel input helps in the training and generalization of the model due to a small dataset and wide variety of environmental conditions The output is an RGB segmented image of crop, weed, and background Python Implementation - Keras, TensorFlow, OpenCV Used my local GPU Nvidia GTX 960m and Google Colab's free GPU (Tesla K80) for training & testing purpose
Weed Detection in Sugar Beet Plants
Implemented a machine learning model for Pixel Wise Semantic Segmentation of weed, crop, and background The model is a CNN based encoder-decoder network with residual blocks The input is an RGB image which is stacked with 11 more channels like vegetation indexes, HSV color space, operators on the indices such as the Sobel derivatives, the Laplacian, and the Canny edge detector The 14 channel input helps in the training and generalization of the model due to a small dataset and wide variety of environmental conditions The output is an RGB segmented image of crop, weed, and background Python Implementation - Keras, TensorFlow, OpenCV Used my local GPU Nvidia GTX 960m and Google Colab's free GPU (Tesla K80) for training & testing purpose
Weed Detection in Sugar Beet Plants
5727479
A system to detect the objects from a test image set containing color and depth images of multiple occluded objects using the orthographic views. https://youtu.be/16kReY8Vna4
Object Detection using Orthographic views of objects
5727472
Solution Python Implementation • Network Used - Convolutional Siamese Network • Framework - Django Working • User requests to upload the real signature to the database using the POST method. • The server processes the request and the full-size signature is uploaded to the database and is stored inside the customer ID folder of the customer. • For verification, the Customer uploads the required input signature using the POST method. • The server fetches the corresponding real image signature from the database using a customer ID. • The model processes both the inputs and generates a vector corresponding to both of them. • Then the distance between the 2 vectors is calculated and the result is predicted based on the corresponding value. • The predicted result in the form of distance which is compared with the threshold and string output is given to the client and it is displayed on the output screen.
Signature Verification
4361104
Leverage IT for Business & build SaaS products.
WordPress - GOTIB
4361175
Website regarding educational services.
WordPress - Smart Computer India
4361239
Designed an elegant dashboard for Video Calling Application.
Dashboard Design

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Deneyim

Performance Engineering Intern

NTT DATA, Japan
Haz 2019 - Tem 2019 (1 ay, 1 gün)
• Worked on Capacity planning, Time Series Forecasting & Anomaly Detection of CPU Usage, web access logs of multiple servers (Application & Database) with more than millions of data records and log file sizes in GBs • Created a standalone web application for the same using voila, jupyter notebook & ipywidgets • Tools & technologies Used - Prophet (Machine Learning), Elastic Stack (Elasticsearch, Kibana, Logstash), InfluxDB, Grafana.

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