Fashion Recommendation System with Deep Learning in Python with Deployment

Build a complete AI-powered Fashion Recommendation System from scratch using Python, PyTorch, and Deep Learning!

In this comprehensive tutorial, you'll create a professional fashion recommendation app that finds similar clothing items using visual features extracted from images with ResNet neural network.

🎯 WHAT YOU'LL BUILD:
✅ Feature extraction using pre-trained ResNet18/50
✅ Image similarity search with K-Nearest Neighbors
✅ Cosine similarity for matching fashion items
✅ Interactive web app with Streamlit
✅ Upload images and get instant recommendations
✅ Filter by category, style, and price
✅ Analytics dashboard with visualizations

🛠️ TECHNOLOGIES & SKILLS:
Python 3.8+
PyTorch & TorchVision (Deep Learning)
ResNet18 (Transfer Learning)
Scikit-learn (Machine Learning)
Streamlit (Web Development)
OpenCV & Pillow (Image Processing)
NumPy & Pandas (Data Science)
Computer Vision concepts
Recommendation Systems

📂 RESOURCES:
🔗 Source Code: [ https://github.com/TensorTitans01/fas... ]
🔗 Dataset Info: [ https://www.kaggle.com/datasets/param... ]
🔗 ResNet Paper: https://arxiv.org/abs/1512.03385
🔗 PyTorch Docs: https://pytorch.org/docs/

💡 PROJECT HIGHLIGHTS:
Real-world application of deep learning
Perfect portfolio project
Scalable to production
Can be monetized
E-commerce ready
Transfer learning best practices
Clean, professional code

🎓 PREREQUISITES:
Basic Python knowledge
Understanding of machine learning concepts (helpful)
No deep learning experience required - I'll explain everything!

🚀 AFTER THIS TUTORIAL:
Add to your data science portfolio
Customize for your niche (shoes, bags, jewelry)
Deploy to Heroku or Streamlit Cloud
Integrate with e-commerce platform
Use real fashion datasets
Fine-tune on custom data
Build a startup around it!

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👨‍💻 ABOUT THIS SERIES:
This is part of my "ML Projects for Beginners" series where we build real-world machine learning applications from scratch. Each project is portfolio-ready and includes complete code, explanations, and deployment guides.

💬 QUESTIONS? DROP A COMMENT!
I read every comment and try to reply within 24 hours. If you get stuck, let me know!

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📱 CONNECT WITH ME:
📷 Instagram: [   / tensortitans   ]
📧 Email: [ tensortitans01@gmail.com ]

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