PyTorch Tutorial – Quick Start Guides for Building AI Models

Did you know you can train a simple image classifier on your laptop in under an hour? PyTorch makes that possible with a clean API and lots of community examples. This page gathers the most useful tutorials so you can jump straight into coding, skip the theory overload, and see results fast.

Why Choose PyTorch?

First, PyTorch feels like regular Python. You write loops, call functions, and see results instantly thanks to dynamic graphs. Second, the library has built‑in tools for loading data, defining layers, and optimizing models, so you spend less time wiring things together. Third, the community shares ready‑made snippets on GitHub and forums, which means you can copy‑paste a training loop and adapt it in minutes.

Because it integrates with NumPy, you can reuse existing data pipelines without rewriting code. The documentation includes clear examples for vision, text, and reinforcement learning, so no matter your project type you’ll find a starter.

Your First PyTorch Project

Let's build a tiny classifier that tells cats from dogs using the CIFAR‑10 dataset. Open a terminal and run pip install torch torchvision. Next, import the libraries and load the data:

import torch
import torchvision
import torchvision.transforms as T

transform = T.Compose([T.ToTensor(), T.Normalize((0.5,), (0.5,))])
train_set = torchvision.datasets.CIFAR10(root='data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)

Define a small neural net with two convolutional layers and a fully connected head:

class SimpleCNN(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = torch.nn.Sequential(
            torch.nn.Conv2d(3, 16, 3, padding=1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(2),
            torch.nn.Conv2d(16, 32, 3, padding=1),
            torch.nn.ReLU(),
            torch.nn.MaxPool2d(2)
        )
        self.fc = torch.nn.Linear(32 * 8 * 8, 10)
    def forward(self, x):
        x = self.conv(x)
        x = x.view(x.size(0), -1)
        return self.fc(x)

Set up the loss function and optimizer, then run a short training loop:

model = SimpleCNN()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(5):
    for images, labels in train_loader:
        outputs = model(images)
        loss = criterion(outputs, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    print(f'Epoch {epoch+1}, loss: {loss.item():.4f}')

After a few epochs you’ll see the loss drop and the model will start guessing correctly. That’s the core workflow – load data, define a model, train, and evaluate. All of the tutorials on this page follow the same pattern, but they add twists like data augmentation, custom loss functions, or mixed‑precision training for speed.

When the basic model works, try swapping layers, adding dropout, or using a pretrained ResNet from torchvision.models. The tutorials guide you through those upgrades step by step, so you never feel lost.

Remember to keep an eye on the GPU usage if you have a graphics card. Running torch.cuda.is_available() tells you if PyTorch can use CUDA, and moving the model and tensors with .to('cuda') gives a big speed boost.

Each tutorial linked below also includes a checklist to verify your environment, common error fixes, and a small project idea to solidify what you learned. Pick the one that matches your skill level, follow the code, and you’ll have a working PyTorch model in no time.

Ready to build? Browse the list, start with the “Beginner PyTorch Hello World” guide, and then move to “Fine‑tuning a Pretrained Network”. The more you code, the faster you’ll spot patterns and debug issues. Happy modeling!