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I'm Harshita Gupta!

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Loves reading, learning, and researching about how AI has revolutionized the human-like task, making it possible to learn from its mistakes.

About Me ⚡

  • 💁‍♀️ Currently working as a Senior Product Engineer at Fosfor - Spectra, LTIMindtree.
  • 👩‍💻 I worked as Python Developer at William O'Neil's and Data Science Intern at Subex - AI Labs.
  • 🧘 I worked as a RESEARCH intern at CSIR-CEERI, Pilani, Rajasthan, India, on Virtual Yoga Trainer via Deep Learning using Human Pose Detection.
  • 🧑‍🎓 Final year at BITS-Pilani, Pilani Campus.
  • 🌱 I’m currently learning Data Build Tool.
  • 🤔 I’m looking for help with Product Analysis.
  • ⛄ Ask me about ML and Analystics .
  • 📧 How to reach me: Gmail Badge
  • ⛓️ Connect to me on Linkedin Badge
  • 🤼‍♀️ I prefer Tensorflow over Pytorch till my current intern.
  • 🧑‍🌾 Helped 200+ people with their queries on the subject at DPhi Community.
  • 📑 Recent Article : Pondering over the methods to clean data in ML-problems.
  • 🤹‍♀️ Fun Fact: I use to keep learning new techniques by participanting in the weekly/monthly Hackathons.
  • 📃 Updated Resume : RESUME

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Harshita's GitHub activity graph Harshita's github statsGitHub Streak

Harshita's github stats

Harshita Gupta's Projects

capsule_network icon capsule_network

This project codes the paper published by Geoffrey E Hinton's in 2016 which uses the capsule networks for identifying any given digit in the image and its reconstruction, section(4)

cement-strength-prediction icon cement-strength-prediction

This project aimed to develop a predictive model for estimating the strength of cement based on various input parameters.

gramoday icon gramoday

This assignment deals with the scrapping of data from the Agricultural Marketing website.

leaf_classification-pytorch icon leaf_classification-pytorch

Leaf-Classification using Pytorch with Custom Training loop and Transfer Learning, achieved a validation accuracy of 0.897 .

pneumoniadetection icon pneumoniadetection

Using an External dataset to get the pre-trained weights of the NIH dataset and training on the provided dataset to detect the presence of pneumonia.

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