This course facilitates the transition from TensorFlow to PyTorch for developers already familiar with TensorFlow. It maps TensorFlow concepts and practices to their PyTorch counterparts, covering tensor operations, model building with torch.nn, data handling via torch.utils.data, and writing custom training loops. You will gain practical experience in translating your TensorFlow skills to effectively develop and train machine learning models in PyTorch.
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.