PyTorch Tutorial

This PyTorch tutorial teaches you deep learning from the ground up using PyTorch, the open-source Python framework behind most modern AI research and many production systems. You will start with tensors and finish by training an image classifier and adapting a pretrained network to your own data.

PyTorch was created at Meta AI and is now governed by the PyTorch Foundation under the Linux Foundation. Libraries such as Hugging Face transformers, torchvision and many LLM training stacks are built on top of it.

What is PyTorch?

At its core PyTorch gives you two things. First, tensors: multi-dimensional arrays like NumPy arrays that can also live on a GPU. Second, automatic differentiation (autograd): PyTorch records the operations you perform and can compute gradients for you, which is exactly what training a neural network needs.

On top of that it offers building blocks for neural networks (torch.nn), optimizers (torch.optim), data loading utilities and a compiler (torch.compile) for speed.

Why Learn PyTorch?

  • It is the dominant framework in research papers and open-source AI models.
  • Code is plain, readable Python that you can debug with print statements.
  • Runs on CPUs, NVIDIA GPUs (CUDA), AMD GPUs (ROCm) and Apple Silicon (MPS).
  • Huge ecosystem: torchvision, torchaudio, Hugging Face, Lightning and more.
  • Skills transfer directly to fine-tuning and serving large language models.

Your First PyTorch Code

import torch

x = torch.tensor([1.0, 2.0, 3.0])
y = x * 2 + 1
print(y)
print(y.sum())
tensor([3., 5., 7.])
tensor(15.)

torch.tensor creates a tensor from a Python list. Arithmetic works element by element, just like NumPy, and every result is another tensor.

What This Tutorial Covers

  • Installing PyTorch and checking for a GPU
  • Tensors, shapes, dtypes and tensor operations
  • Autograd and gradients
  • Building models with nn.Module, loss functions and optimizers
  • Writing a training loop with Dataset and DataLoader
  • Training on CUDA and Apple MPS devices
  • A complete MNIST handwritten-digit classifier and a CNN
  • Saving and loading models, transfer learning and practical tips

Prerequisites

You need basic Python (functions, classes, lists) and a little high-school math. Familiarity with NumPy helps but is not required. Every example here is a short script; outputs that involve randomness, such as training losses, are marked as sample output because your numbers will differ.

Frequently asked questions

What is PyTorch used for?

Building and training neural networks for computer vision, natural language processing, speech, recommendation systems and large language models, in both research and production.

Is PyTorch better than TensorFlow?

Both are capable, but PyTorch is now the more common choice in research and open-source AI, and most new models are released for PyTorch first. TensorFlow and Keras remain in use in some production systems.

Is PyTorch hard to learn?

If you know Python, the basics are approachable because PyTorch code is ordinary Python. The harder part is understanding deep learning concepts, which this tutorial explains step by step.