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To learn PyTorch in Python, start with tensors, then understand how automatic differentiation calculates gradients, and use those gradients to train a small neural network. Follow the official beginner tutorial’s workflow—prepare data, define a model, calculate loss, optimize parameters, and save or load the model—using a hosted notebook or a local installation.
What PyTorch does in Python
PyTorch is a Python framework for working with tensors and building and training neural networks. A tensor is an n-dimensional data structure: it can represent a scalar, a vector, a matrix, or data with more dimensions. PyTorch provides operations on tensors and supports running them on GPUs.
The official Learning PyTorch with Examples tutorial compares a tensor’s broad structure to a NumPy array. The two are not interchangeable in every respect: PyTorch also provides automatic differentiation for calculating gradients, and its neural-network tools help organize models and loss functions.
What you need before you begin
- Basic Python: Be comfortable reading and writing functions, working with variables, and using common Python data structures.
- Some machine-learning context: It helps to know what a model, loss, and training step mean, but you can learn these ideas as you work through the tutorial.
- A place to run Python: Use a hosted notebook to get started without setting up a local environment, or install PyTorch on your computer.
The official Learn the Basics tutorial says it assumes basic familiarity with Python and deep-learning concepts. If those topics are new, work through the examples slowly and treat unfamiliar terms as concepts to learn rather than prerequisites you must already master.
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Choose a hosted notebook or local setup
Hosted notebook
A hosted notebook is a convenient first step if you want to follow examples without choosing a local package or hardware configuration. The official beginner guide offers notebook execution as an option. It is also useful when you want to focus on the code before managing your own environment.
Local installation
For a local setup, use the official PyTorch installation selector. Choose the options that match your operating system, package manager, language, and compute platform. The page distinguishes stable and preview builds and provides platform-specific commands; there is no single CPU, CUDA, or ROCm command that is correct for everyone.
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The installation page stated that the latest stable PyTorch requires Python 3.10 or later when checked on October 7, 2026. That requirement can change with releases, so check the live selector before installing. Select a compatible accelerator option only when it matches your hardware and software setup; otherwise, choose an appropriate CPU option in the selector.
Learn PyTorch in this order
The official beginner guide uses FashionMNIST to demonstrate a complete workflow. Follow its sequence so that each new concept has a clear role in training a model.
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- Load and prepare data. Learn how examples and labels are organized, then prepare them in the form a model can process.
- Work with tensors. Inspect shapes and practice operations on the data. Tensor dimensions matter: a model expects inputs in a particular arrangement.
- Define a model. Start with a small neural network and learn how its layers transform input data into predictions.
- Calculate a loss. A loss function measures how far predictions are from the desired results. It gives training a quantity to reduce.
- Use automatic differentiation. PyTorch tracks the operations involved in a calculation and can compute gradients needed to adjust model parameters.
- Optimize parameters. Use an optimizer to update the model using those gradients, then repeat the training process.
- Save and load the model. Practice preserving trained parameters and restoring them so you can use the model again.
Use the guide’s examples to connect each step to executable code rather than trying to memorize a complete training loop before understanding what its parts do.
Understand tensors, autograd, and torch.nn
Tensors: represent data and computations
PyTorch tensors hold the data used in calculations, including model inputs and parameters. They resemble NumPy arrays in their n-dimensional structure, but PyTorch tensors are designed to work with the framework’s gradient and device features. Learn to check a tensor’s shape and understand how operations change it; many beginner errors come from mismatched dimensions.
Autograd: calculate gradients
Automatic differentiation, available through PyTorch’s autograd system, calculates how a result changes when tensor values change. During training, those gradients indicate how model parameters can be adjusted to reduce the loss. This is the bridge between computing a prediction and learning from its error.
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torch.nn: organize neural networks
The torch.nn package supplies modules and loss functions for building neural networks. It provides a more structured way to express a model than writing every operation as raw tensor code. Learn the tensor and gradient ideas first, then use torch.nn to define models and losses in the beginner workflow.
Choose the right learning mode as you progress
- For the least setup: Start in a hosted notebook and follow the step-by-step basics tutorial.
- For practicing in your own environment: Install locally with the official selector, matching the command to your operating system and compute platform.
- For learning concepts in sequence: Use the beginner guide’s data-to-model workflow and make sure you understand each stage before moving on.
- For seeing an idea in a compact example: The official examples tutorial presents concepts in self-contained code examples. Use it to reinforce the basics, not as a substitute for learning what each operation does.
The official sources distinguish these learning formats and tools but do not establish that one hardware choice is universally faster or better. Choose a compatible setup that meets your needs rather than assuming a GPU is required to begin.
Common beginner mistakes to avoid
- Copying an installation command without checking its options. A command intended for one operating system or accelerator may not fit another. Generate the command from the official selector.
- Treating PyTorch and NumPy arrays as identical. Their n-dimensional data structures are similar in broad shape, but PyTorch adds framework features such as automatic differentiation and GPU execution.
- Jumping straight to model code. Learn how data is prepared, how a loss is computed, and how gradients update parameters; those steps explain what a training loop is doing.
- Assuming a GPU is necessary. Begin with an appropriate available setup. An accelerator choice must match the machine and software configuration.
- Expecting the basics tutorial to cover every advanced task. Its introductory workflow is a starting point, not a complete guide to deployment, distributed training, compilation, performance tuning, or all accelerator configurations.
What to do after the beginner tutorial
Once you can follow the FashionMNIST example from prepared data through saving a model, revisit the code and explain each part in your own words: what the tensors contain, what the model predicts, how the loss is calculated, where gradients come from, and how the optimizer changes parameters. Then adapt one part at a time, such as inspecting the data differently or changing the model structure, so you can identify which change produced which effect.
For later work, use the official tutorials as your next reference point and choose material aimed at the specific task you want to learn. The introductory workflow does not by itself teach deployment, distributed training, compilation, or performance tuning.
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