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Shreyas

Deep Learning Engineer

I'm an engineer with a passion for physics, artificial intelligence, and data science. My skills include programming in Python and Julia; experience with machine learning libraries such as Scikit-Learn and PyTorch; and knowledge of data analysis and applied math.

I've developed a form of artificial curiosity in feed-forward networks for enhanced image classification, and I'm excited about exploring new ways to apply machine learning to solve real-world problems. In physics and engineering, I've developed a "Two-Point Masses" model which serves as a theoretical framework for designing frictionless braking systems based on the principle of conservation of angular momentum.

Currently, I specialize in generative adversarial networks (GANs), image inpainting systems like LaMa (large mask inpainting system), and deep learning-based image segmentation systems. I'm enthusiastic ⚡ about using my skills and knowledge to contribute to science, and I'm always looking for new opportunities to collaborate with others. If you're interested in learning more about my work, feel free to reach out and connect with me or explore my projects. Let's see where we can take our shared interests! 😃

Skills

Python PyTorch PyTorch-Lightning Julia NumPy Pandas Matplotlib Seaborn Scikit-Learn Hugging Face OpenCV  Linux Docker Git  MLflow AWS Streamlit Hydra HTML5 CSS3 PostgreSQL Flask Power BI Excel Jira Notion

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Shreyas's Projects

a-curious-ffn icon a-curious-ffn

This project utilizes Artificial Curiosity in a Feed-Forward Network to recognize manipulated images.

anywherecalc icon anywherecalc

Perform basic arithmetic calculations anywhere on your computer.

california-house-price-prediction icon california-house-price-prediction

This is a guided project from the book "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition" by Aurelien Geron.

cv icon cv

Portfolio Website

emnist-cnn icon emnist-cnn

This project involves the use of a Convolutional Neural Network (CNN) to classify handwritten characters in the EMNIST dataset.

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