courseraАнализ данных

Optimize AI: Fine-Tune & Maximize Accuracy

Курс от Coursera

Средний≈ 2.2 чАнглийский

О курсе

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 logging, and training-curve interpretation, you’ll practice making informed decisions about which layers to update, which LR schedule to select, and how to balance accuracy with training efficiency. By the end of the course, you’ll be able to fine-tune transformer-based models effectively and choose learning-rate strategies that reduce training time without sacrificing performance.

Навыки, которые вы освоите

Fine-tuning Vision Transformer (ViT)Model Deployment Keras (Neural Network Library)Model Optimization Predictive Modeling Model Training MLOps (Machine Learning Operations)

Программа курса

1 модулей · 14 учебных материалов

01Optimize AI: Fine-Tune & Maximize Accuracy14 материалов

Fine-Tuning ViT-B/16 with Transfer Learning for Domain-Specific Datasets

Fine-Tuning ViT-B/16 with Transfer Learning for Domain-Specific DatasetsDIALOGUE Introduction and WelcomeВидео Why Transfer Learning Accelerates Vision TrainingВидео Walkthrough: Unfreezing the Final Four Transformer Blocks in KerasВидео

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