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Sunilyadav03/README.md

πŸ‘‹ Hi there, I'm Sunil Kumar Yadav (@sunil003)!

πŸŽ“ Final-Year MSc Data Science Student @ the Defence Institute of Advanced Technology(DIAT), DRDO, Pune.
πŸ’» AI Researcher | Machine Learning Enthusiast | Data Science Enthusiast
πŸš€ Passionate about solving complex problems using Data Science, Machine Learning, and Deep Learning.


🌟 About Me

I am a data science student currently specializing in Continual Learning, with a focus on:

  • Continual Learning for Regression Tasks (CLeaR)
  • Regularization-Based Techniques (e.g., Elastic Weight Consolidation - EWC)
  • Architecture-Based Techniques (e.g., Progressive Neural Networks - PNN)
  • Replay-Based Techniques

Additionally, I have been working on multiple projects involving:

  • Large Language Models (LLMs)
  • Optical Character Recognition (OCR)
  • Deepfake Image Detection using VGG, ResNet, InceptionNet and other own custom architectures.

Profile Views

πŸ›  Languages and Tools

Python PyTorch[ Pandas Matplotlib Scikit-Learn Seaborn VS Code Git R


πŸ” What I’m Interested In

  • Machine Learning & Deep Learning: Building and improving models to solve real-world problems.
  • Continual Learning: Exploring techniques like EWC, PNN, and Replay-Based Methods to enable models to learn continuously without forgetting.
  • Generative Models: Working with GANs, VAEs, and Diffusion Models to push the boundaries of AI creativity.

🌱 What I’m Currently Learning

  • LLMs: Novel approaches to build domain specific LLM models.
  • Advanced NLP: Deepening my knowledge of Transformer Models like GPT.
  • Generative AI: Exploring cutting-edge advancements in GANs, VAEs, and Diffusion Models.

πŸ“‚ Projects

Continual Learning

  • Continual Learning for Regression Tasks (CLeaR): Extending continual learning to regression problems.
  • Elastic Weight Consolidation (EWC): Regularization-based approach to prevent catastrophic forgetting.
  • Progressive Neural Networks (PNN): Architecture-based method for continual learning.
  • Replay-Based Techniques: Using stored data to retain knowledge.

Deepfake Detection

  • Developing models using VGG, ResNet, and Own Custom Architectures to detect deepfake images with high accuracy.

Large Language Models (LLMs)

  • Exploring LLMs for various NLP tasks, including text generation, summarization, and question-answering.

Optical Character Recognition (OCR)

  • Building robust OCR systems to extract text from images and documents.

πŸ“« How to Reach Me

πŸ“§ Email: [email protected]

πŸ”— LinkedIn: Let’s Connect!

πŸ™ GitHub: @sunil003)

πŸ”— Medium: Sunil


πŸš€ Let’s Build the Future of AI Together!

I am always open to collaborating on exciting projects and discussing new ideas in AI, ML, and Data Science. Feel free to reach outβ€”let’s create something amazing!


⭐️ Star this repo if you find it interesting! ⭐️


Thank you for visiting my profile! πŸ˜„

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    Forked from agno-agi/agno

    Build multi-modal Agents with memory, knowledge, tools and reasoning. Chat with them using a beautiful Agent UI.

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  2. Video2Text Video2Text Public

    Video2Text - Easily convert your video to text

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  3. Sorting_Techniques Sorting_Techniques Public

    This repository contains implementations of three fundamental sorting algorithms in Python: Bubble Sort, Insertion Sort, and Selection Sort.

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  4. DeepFake-Image-Detection DeepFake-Image-Detection Public

    This repository provides a robust solution for detecting deepfake images using state-of-the-art deep learning models like VGG16, VGG19, InceptionV3, and ResNet50. this open-source tool is ready to …

    Jupyter Notebook 1

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