Free to read ยท Three levels
PyTorch: tensors, gradient state, and precise execution contracts
A complete framework-specific course in tensor ownership, autograd, registered state, data and loss interfaces, training control, attention, restoration, profiling, compilation, and export.
Start learningIn this course
Learn PyTorch through explicit contracts for tensors, gradients, modules, data, objectives, and execution state. Eighteen developed lessons connect CPU tensor construction, autograd modes, parameters and buffers, losses, loaders, controlled updates, optimizer state, accumulation, spatial and sequence layouts, attention masks, reproducibility, views, loading, profiling, compilation, export, and complete synthetic inference acceptance. Every lesson includes five developed sections, two explained examples, three diagnostic mistakes, and two complete worked exercises.
References are pinned to the official PyTorch 2.14 documentation as an API documentation target. No torch runtime is installed in the review environment. Each lesson therefore pairs an explicitly unexecuted CPU-only framework program, with runnable:false and reasoned expected output, with an executed deterministic Python standard-library companion. The companions validate only their own arithmetic or contracts and do not establish PyTorch behavior. No framework installation, model download, external dataset, GPU, service, cloud resource, or credential is required or used.
What you will learn
- Explain tensor shape, dtype, device, storage ownership, views, and broadcasting.
- Trace autograd participation, gradient buffers, registered module state, and optimizer membership.
- Define valid loss, data-loader, convolution, sequence, and attention interfaces.
- Preserve accumulation, clipping, scheduler, randomness, and restoration boundaries.
- Distinguish documented profiling, compilation, export, and loading contracts from actual execution evidence.
Your learning path
Three levels
Tensor, gradient, and module foundations
CPU tensors, autograd modes, registered state, loss targets, data batches, and one controlled update.
- CPU tensors, dtypes, and shapes
- Autograd and gradient modes
- Modules, parameters, and buffers
- Losses, targets, and reductions
- Datasets, loaders, and collation
- A controlled training and evaluation step
Training state and architecture interfaces
Optimizer groups, accumulated objectives, spatial and sequence contracts, attention masks, and reproducible continuation.
- Optimizers, parameter groups, and state
- Accumulation, clipping, and schedules
- Convolutions and spatial layout
- Embeddings, sequences, and lengths
- Attention masks and batch layout
- Randomness, reproducibility, and resumption
Storage, restoration, and transformed execution
Views, reviewed loading, resource evidence, compilation, export domains, and complete synthetic acceptance.
- Views, contiguity, and in-place updates
- State dictionaries and reviewed loading
- Profiling and resource evidence
- Compilation and graph-break contracts
- Export and dynamic-shape contracts
- Complete synthetic model acceptance
Who this is for
- Python developers and analysts who want to implement and inspect PyTorch-specific model contracts.
- Learners with neural-network foundations who need precise shape, state, gradient, and runtime reasoning.
Before you start
- Python functions, classes, loops, lists, dictionaries, exceptions, and basic JSON.
- Vectors, matrices, derivatives, means, and elementary floating-point reasoning.
- Deep-learning and machine-learning concepts including losses, optimization, held-out evaluation, leakage, and model selection.