Getting Started with PyTorch

Welcome to the PyTorch getting started guide! PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing. In this guide, you will learn the basics of PyTorch and how to get started with your first project.

Installation

Before you start, make sure you have Python installed on your system. You can download and install Python from the official website.

Next, install PyTorch by following the instructions on the PyTorch website.

Hello World

Let's start with a simple example. In this example, we will create a neural network that predicts the output of a simple function.

import torch
import torch.nn as nn

# Define a simple neural network
class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.linear = nn.Linear(1, 1)

    def forward(self, x):
        return self.linear(x)

# Create an instance of the network
model = SimpleNN()

# Create some input data
x = torch.tensor([[1.0], [2.0], [3.0]])

# Perform a forward pass
output = model(x)

print(output)

Next Steps

Now that you have a basic understanding of PyTorch, you can explore more advanced topics such as:

• Data Loading and Preprocessing
• Training and Evaluating Models
• Using GPU Acceleration

Remember to check out the PyTorch documentation for more information and resources.

Community

PyTorch has a vibrant and active community. You can join the PyTorch forums to ask questions, share your experiences, and learn from others.

PyTorch Community