Welcome to the PyTorch tutorials section! Here, you'll find a wealth of resources to help you learn how to use PyTorch, a popular open-source machine learning library.

Getting Started

Before diving into the tutorials, make sure you have PyTorch installed. You can download and install it from the official PyTorch website.

Tutorials

Introduction to PyTorch

PyTorch is a flexible deep learning library that provides a wide range of features for building and training neural networks. It is known for its ease of use and dynamic computation graph.

What is PyTorch?

PyTorch is an open-source machine learning library based on the Torch library, developed by Facebook's AI Research lab. It is widely used for deep learning applications and is known for its ease of use and dynamic computation graph.

Why use PyTorch?

  • Ease of use: PyTorch is designed to be intuitive and user-friendly, making it easy for beginners to get started with deep learning.
  • Dynamic computation graph: PyTorch uses a dynamic computation graph, which allows for more flexibility and ease of debugging.
  • Strong community support: PyTorch has a large and active community, providing a wealth of resources and support.

![PyTorch Logo](https://cloud-image.ullrai.com/q/PyTorch Logo/)

Building your first neural network

In this tutorial, we'll walk you through the process of building your first neural network using PyTorch. We'll cover the basics of neural networks, including data loading, model definition, and training.

Data Preparation

To build a neural network, we need to start with some data. Let's use the MNIST dataset, which contains 60,000 images of handwritten digits (0-9).

import torch
from torchvision import datasets, transforms

# Define a transform to normalize the data
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])

# Download and load the training data
trainset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

# Download and load the test data
testset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)

Model Definition

Now that we have our data ready, we can define our neural network. In this example, we'll create a simple feedforward neural network with one hidden layer.

import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(28*28, 500)
        self.fc2 = nn.Linear(500, 10)

    def forward(self, x):
        x = x.view(-1, 28*28)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

net = Net()

Training

Now that we have our model defined, we can proceed to train it. We'll use the Adam optimizer and the cross-entropy loss function.

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)

for epoch in range(10):  # loop over the dataset multiple times
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:    # print every 100 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

print('Finished Training')

Advanced tutorials

For more advanced tutorials, check out the PyTorch documentation.

Advanced Tutorials