I'm a Deep Learning 🤖 + GenAI enthusiast 🌌
I love building intelligent systems, applied ML projects, and interactive AI apps 🚀
- Languages: Python, C++, C
- ML/DL: LangChain, TensorFlow, PyTorch, Keras
- Data Science: Scikit-learn, Pandas, NumPy, Matplotlib
- Web/App: React, Streamlit, Flask, FastAPI
- Other Tools: SQL, Git, Bash
- EduEase — Built with LangChain to orchestrate NLP workflows, combining transformer-based summarization for lesson plans, text classification for wellness insights, and rule-based MCQ generation, fully deployed on Render.
- VibeTrack — Built an end-to-end emotion recognition pipeline using deep CNNs on audio features, achieving 99.9% validation accuracy, with real-time prediction and personalized suggestion generation.
- QueryClone-Detector — Implemented duplicate question detection using Bag-of-Words embeddings with Logistic Regression, optimizing similarity detection for large-scale Q&A datasets.
- ML Notes Helper — Designed an LSTM-based sequence model for predictive text generation, capable of auto-completing up to 20 words in context-aware academic notes.
- 🌱 Exploring Generative AI, Transformers, Emotion AI, and Agentic AI Systems.
- 💼 LinkedIn: Vennela Varshini Anasoori
- 📂 GitHub: vennelavarshini18
- 📖 Kaggle: vennela18
⚡ Always learning, always building — turning AI ideas into reality.