PyTorch Fundamentals
PyTorch is a Python framework for building and training neural networks. You use tensors, automatic differentiation, and reusable model components to turn data and a learning objective into a trained model.
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Intro
PyTorch Fundamentals
PyTorch is a framework for numerical computing and machine learning. Its central data type is the tensor. A tensor holds numbers in one or more dimensions and can run on supported accelerators as well as CPUs.
The framework makes a training system concrete. You represent data as tensors. You express a model as a composition of operations. You define a loss that measures error. PyTorch records the operations needed for gradients, and an optimizer uses those gradients to update learnable parameters.
What PyTorch gives you
PyTorch has a small core and a broad ecosystem. The core provides tensor operations, automatic differentiation through torch.autograd, neural-network building blocks in torch.nn, and optimization algorithms in torch.optim.
You write ordinary Python around that core. This matters when you need to inspect intermediate values, branch on data, or debug a training step. The cost is that you still own the training design. PyTorch does not choose the dataset, objective, model architecture, evaluation metric, or deployment boundary for you.
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Sources
- https://pytorch.org/
Supports
- PyTorch installation and project ecosystem
- PyTorch ecosystem examples including Captum and PyTorch Geometric
- https://docs.pytorch.org/tutorials/beginner/basics/intro.html
Supports
- Complete workflow spanning data, models, optimization, and saving
- Official Learn the Basics study sequence
- https://docs.pytorch.org/tutorials/beginner/basics/tensorqs_tutorial.html
Supports
- Tensor attributes, operations, and accelerator use
- Shape, data type, and device as tensor properties
- https://docs.pytorch.org/tutorials/beginner/basics/data_tutorial.html
Supports
- Dataset and DataLoader responsibilities
- Batching and iteration over data
- https://docs.pytorch.org/tutorials/beginner/basics/buildmodel_tutorial.html
Supports
- nn.Module, layers, parameters, and forward computation
- Training and evaluation module modes
- https://docs.pytorch.org/tutorials/beginner/basics/autogradqs_tutorial.html
Supports
- Recorded computation and gradient calculation
- Backward calculation and gradient tracking
- https://docs.pytorch.org/tutorials/beginner/basics/optimization_tutorial.html
Supports
- Loss calculation, backward passes, optimizer steps, and gradient clearing
- Standard training-loop structure
- https://docs.pytorch.org/tutorials/beginner/basics/saveloadrun_tutorial.html
Supports
- state_dict saving and loading
- Inference preparation and evaluation mode
- https://docs.pytorch.org/tutorials/intermediate/torch_compile_tutorial.html
Supports
- torch.compile as a later optimization topic
- https://github.com/bharathgs/Awesome-pytorch-list
Supports
- Captum and Lightning ecosystem discovery