Advanced · Cohort
Deep Learning Introduction
From perceptrons to small CNNs and embedding models. Emphasis on training discipline, GPU etiquette, and debugging loss curves.
Request informationFeatures
- PyTorch tensors and autograd essentials
- CNN lab on CIFAR-scale data
- Text classification with embeddings
- Learning rate schedules and early stopping
- GPU lab access during scheduled windows
- Model card template for capstone
Outcomes
- Train a CNN with documented hyperparameters
- Debug a failing training run using provided checklist
- Complete a model card for one capstone model
Dr. Yuki Tanaka
Former research engineer; 9 years teaching applied ML in Tokyo.
FAQ
Scheduled cloud GPU windows included; personal GPU setup guide optional.
Reviews
"Loss-curve debugging checklist saved my capstone week."