Full Stack Deep Learning course notes covering ML production from data to deployment.
Full Stack Deep Learning
Full Stack Deep Learning (fullstackdeeplearning.com) — Notes for week 1 to week 12 (2021)
Reference: https://fullstackdeeplearning.com/spring2021
Underfitting vs Overfitting
Underfitting (reducing bias): - Bigger model - Reduce regularization - Error analysis - Different model architecture - Tune hyper-parameters - Add features
Overfitting (reducing variance): - Add more training data - Add normalization (batch norm, layer norm) - Add data augmentation - Increase regularization (dropout, L2, weight decay) - Error analysis - Choose different model architecture - Tune hyper-parameters - Early stopping - Remove features - Reduce model size
StackDeepLearning #computervision #AI #deeplearning
Source: Pirahansiah, F. “Full Stack Deep Learning”. pirahansiah.com, updated 2026-09-19. Canonical URL https://pirahansiah.com/farshid/content/course-full-stack-dl/ · markdown source course-full-stack-dl.md · how to cite, quote and link this site.