Hi there, my name is Ankur
I'm a Python and Machine Learning Developer.

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About me

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I am a Final year under-grad student pursuing B.Tech in Electronics and Communication Engineering from U.I.E.T. Kurukshetra University, Kurukshetra (79.9% aggregate)

I love to work with technologies like Machine Learning, Deep Learning, Cloud Computing, Data Analytics, IoT, MLops and Databases. Occasionally, I also work on MATLAB, C/Cpp, Embedded Systems and Robotics.
I also love to participate in Hackathons, I recently won Smart India Hackathon(SIH'20), where I and my team were working on a project to resolve the stray cattle issue

In my free time, I love to cook, read about ancient architecture and do gardening.

Tools and Frameworks I love to work with:

Python 3TensorflowKerasSK_LearnOpenCVTableauGCPAzureDockerKubernetes

MATLABArduinoRaspberry PIMySQLSQLiteFirebaseGitGitHubLinux-UbuntuVisual Code

View Resume


Face Recognition - Research Project

This project can take input images and store them as a numpy file. The test Algorithm forms different clusters based on photos of various people and when a test image is served, the algorithm uses KNN to classify it. SVM (with kernels) and CNN can also be used for classification.
Currently, I am also doing research project on topics like Effects of Image processing and GAN's on real time Face recognition (Large number of Subjects, Low Per subject Data) using Deep CNN and its deployment on Edge devices.

Source Code on GitHub

Generative Adverserial Networks using DCNN

GANs are a clever way of training a generative model by framing the problem as a supervised learning problem with two sub-models: the generator model that we train to generate new examples, and the discriminator model that tries to classify examples as either real (from the domain) or fake (generated). The two models are trained together in a zero-sum game, adversarial, until the discriminator model is fooled about half the time, meaning the generator model is generating plausible examples.
I have worked with GAN on many datasets like MNIST, Pokemon, Anime, LFW, ORL etc.

Source Code on GitHub

Image Caption Generator using LSTM and DCNN

Image caption generator is a task that involves computer vision and natural language processing concepts to recognize the context of an image and describe them in a natural language like English.
DCNN and LSTM are used for image analysis and caption generation respectively.

Source Code on GitHub


In order to reduce pressure on Doctors, this application which is a part of COVID DOCTOR that helps people to recognise the possibility of COVID-19 infection by analysing their X-Ray. In addition to COVID-19 virus, this application also classifies few other types of flu. The model is more than 95% accurate

Source Code on GitHub


Drop me a mail, if you need help with some project or want to collaborate/ communicate with me