Prerequisites: TensorFlow experience required
Level:
Translate TensorFlow Concepts
Map core TensorFlow components (Tensors, Graphs, Layers, Optimizers) to their PyTorch equivalents.
Master PyTorch's Define-by-Run Paradigm
Understand and utilize PyTorch's dynamic computation graphs for flexible model building.
Develop PyTorch Models
Construct, train, and evaluate neural networks using torch.nn
, torch.optim
, and custom training loops.
Manage Data Pipelines
Implement efficient data loading and preprocessing pipelines using torch.utils.data
and torchvision.transforms
.
Handle Model Persistence
Save, load, and prepare PyTorch models for deployment, including an introduction to TorchScript.
Adapt TensorFlow Workflows
Transition existing TensorFlow workflows and thought processes to the PyTorch ecosystem.