Optimize AI: Fine-Tune & Maximize Accuracy

This course teaches you how to fine-tune powerful vision models and optimize their training for real-world performance. You’ll start by applying transfer learning with a pre-trained ViT-B/16 model, learning how to freeze and selectively unfreeze layers to adapt general visual representations to domain-specific datasets such as retail product images. You’ll then analyze and compare learning-rate schedules, including cosine decay and the one-cycle policy, to understand how each strategy shapes training stability, convergence speed, and validation accuracy. Through hands-on labs, experiment loggi…