💻 AI & ML Enthusiast • 🚀 Project Builder • 🧠 Deep Learning Explorer • 🔬 ML Researcher

• 🎓 Passionate about Artificial Intelligence, Machine Learning, Generative AI Agents, and Computer Vision
• 🛠 I love building end-to-end projects — from dataset collection and training models to real-world deployment
• 🌱 Currently exploring LangChain, RAG, and AI agents
• 📊 Enjoy working with research-backed tools and frameworks — especially in healthcare & automation domains
• ✍️ I write code that learns and think about how to make machines think!

Here are some of the exciting projects I've been working on:

• 🔍 Classification-Of-CIFAR10-Dataset
  Trained a deep Convolutional Neural Network (CNN) on the CIFAR-10 dataset, with custom image testing and performance evaluation.

• 🎨 Custom Image Generator (0–9)
  Built a Conditional GAN (cGAN) from scratch to generate handwritten digits (0–9) using the MNIST dataset. Supports conditional digit generation and GPU-accelerated training with PyTorch Lightning.

• 🧬 Skin-Cancer Detection
  Developed a CNN-based model for classifying pigmented skin lesions using the HAM10000 dataset. Useful for exploring medical AI and computer vision applications in healthcare.

• 👁️ Eye Disease Classification
  Built a deep learning model to classify various eye diseases from retinal images. Helps in early detection and research for ophthalmology applications.

• 🐍 Snake Game
  Classic Snake game implemented in HTML, CSS, and JavaScript. Includes score tracking and responsive controls.

• 📚 LeetCode & DSA Solutions
  Curated collection of algorithm and data structure solutions for problem-solving practice, implemented in Python.

• 🧬 Coming Soon: Medical AI Assistant
  Building a LangChain + Groq API-based assistant for personalized healthcare management.
  (Features: patient data, reminders, recovery tracking, emergency alerting)

• 📚 Research-focused AI Agents
  Reinforcement-learning agent for real-world task planning and dataset integration.

Exploring LangChain, Poetry, LLM agents, RAG (Retrieval-Augmented Generation), Vector Databases, Chroma, LangGraph, Groq APIs, Gemini-powered agents, AI assistants, and other agentic AI technologies for building intelligent, autonomous systems in healthcare, automation, and real-world applications.

Diving deeper into Deep Learning frameworks like PyTorch, Scikit-learn, and Keras to strengthen AI/ML project development and research.

Languages    : C, C++, Java, Python, SQL, Bash, Shell Scripts in Linux
Frameworks   : PyTorch, TensorFlow, FastAPI, Flask
AI/ML        : LangChain, PyTorch, Transformers, CNNs, Regression Models, Vector DBs
Tools        : Git, Docker, Google Colab, Jupyter, Vercel, Cassandra