AI Meal Planning: Take a Fridge Photo -> Get a Recipe

Introduction to Image Processing

• Digital Image Representation and Preprocessing 💻 45min
• Color Spaces and Feature Extraction 💻 30min
• Noise Reduction and Image Enhancement Techniques 💻 45min

Deep Learning Fundamentals

• Convolutional Neural Networks (CNN) Architecture 💻 60min
• Activation Functions and Loss Optimization 💻 45min

Localization Techniques

• YOLO (You Only Look Once) Architecture Overview 🔒
• Bounding Box Regression and Non-Max Suppression 🔒
• Instance Segmentation with Mask R-CNN 🔒

Dataset Management

• Data Annotation Strategies for Food Items 🔒
• Data Augmentation for Fridge Lighting Conditions 🔒
• Transfer Learning with Pre-trained Models 🔒

LLM Integration

• Prompt Engineering for Culinary Context 🔒
• Retrieval-Augmented Generation (RAG) for Recipes 🔒
• Fine-tuning LLMs on Recipe Datasets 🔒

Building the Pipeline

• Designing the API with FastAPI 🔒
• Model Serving with Docker and Kubernetes 🔒
• Cloud Deployment Strategies (AWS/GCP) 🔒

End-to-End Implementation

• Project Scoping and Requirements Definition 🔒
• Building the Fridge-to-Recipe Pipeline 🔒
• Testing, Evaluation, and User Feedback Loops 🔒
• Final Presentation and Model Optimization 🔒