I specialize in creating practical AI and machine learning tutorials that help developers understand complex concepts like LLMs, GPUs, RAG systems, and deep learning architectures.
My goal is to bridge the gap between AI research and real-world implementation. 🔬 Current Focus 🤖 Large Language Models (LLMs) 🧠 Retrieval Augmented Generation (RAG) 👁 Computer Vision & YOLO Models ⚡ GPU Computing & CUDA 🧩 AI Infrastructure & Model Deployment 🛠️ Tech Stack
📊 GitHub Stats 🔥 GitHub Streak 📈 Most Used Languages
Write developer-focused AI tutorials Build hands-on ML projects Explain complex AI systems simply Create GPU-based deep learning guides
Fine-tuning YOLO models Building RAG pipelines Working with open-source LLMs Deploying AI workloads on GPUs
AI Agents & Memory Architectures Model Quantization & Optimization Advanced Computer Vision Production ML Pipelines
I’m interested in collaborating on: AI / ML tutorials Computer vision projects Open-source AI tools GPU infrastructure projects 📫 Connect With Me
https://github.com/smukherjee1do ⭐ "Making AI easier to understand, build, and deploy." 🛒 YOLO26 Retail Object Detection Fine-tuned YOLO26 on a custom retail dataset to detect products on store shelves.
Highlights Custom dataset training GPU training workflow Retail shelf monitoring use case Jupyter + Python implementation
🔗 https://github.com/smukherjee1do/yolo26-retail-detection
🧠 LangGraph + Mem0 AI Memory System Implementation of long-term memory for AI agents using LangGraph and Mem0. Highlights
Persistent memory architecture Memory retrieval pipelines Agent memory optimization Production design patterns
🔎 Retrieval-Augmented Generation (RAG) System
Highlights Document ingestion pipeline Vector database integration Context retrieval optimization LLM response generation
⚡ GPU-Based Deep Learning Workflows Collection of projects demonstrating training and inference workflows on GPUs. Includes CUDA basics Deep learning training pipelines GPU benchmarking experiments
I also publish developer-focused AI tutorials covering: Deep learning concepts Computer vision models LLM architecture GPU infrastructure AI deployment
