PyTorch / Training state and architecture interfaces
PyTorch — Training state and architecture interfaces
Optimizer groups, accumulated objectives, spatial and sequence contracts, attention masks, and reproducible continuation.
What you will learn
- Optimizer groups, accumulated objectives, spatial and sequence contracts, attention masks, and reproducible continuation.
Optimizers, parameter groups, and state
Make the optimizer’s parameter membership, per-group options, and continuation state explicit.
Start learningAccumulation, clipping, and schedules
Preserve objective weighting and optimizer-step boundaries when changing microbatch processing.
Start learningConvolutions and spatial layout
Make PyTorch channel conventions, kernel connectivity, and spatial alignment explicit.
Start learningEmbeddings, sequences, and lengths
Keep token identity, learned lookup state, padding, and recurrent layout contracts distinct.
Start learningAttention masks and batch layout
Read each PyTorch attention API’s shape and mask polarity instead of transferring assumptions by name.
Start learningRandomness, reproducibility, and resumption
Specify the state and comparison required for replay instead of treating one seed as a complete experiment.
Start learning