Sure, here's a step-by-step guide on how to get started with PyTorch:

1. Install Python: PyTorch requires Python, so you'll need to download and install it first. You can download Python from the official Python website (https://www.python.org/downloads/).

2. Install PyTorch: Once you have Python installed, you can install PyTorch using pip, which is a package manager for Python. Open a command prompt or terminal and run the following command:
`pip install torch`
This will install the latest version of PyTorch.

3. Choose an IDE: You'll need an integrated development environment (IDE) to write and run your PyTorch code. There are many options to choose from, such as PyCharm, Visual Studio Code, or Spyder. Choose the one that you're most comfortable with.

4. Import PyTorch: Open your IDE and start a new Python project. In your Python script, import the PyTorch library using the following command:
`import torch`

5. Define your neural network: Define the architecture of your neural network by creating a subclass of the `nn.Module` class in PyTorch. This will allow you to define the layers of your network, the activation functions, and the forward pass. Here's an example of a simple neural network with one input layer, one hidden layer, and one output layer:
```python
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(1, 10)   # input layer
        self.fc2 = nn.Linear(10, 1)   # output layer

    def forward(self, x):
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        return x
```
This neural network has one input layer, `fc1`, with one input neuron, ten hidden neurons, `fc2`, and one output neuron.

6. Prepare your data: Before you can train your neural network, you'll need to prepare your data. This typically involves loading the data, splitting it into training and testing sets, and normalizing the data if necessary.

7. Train your neural network: Train your neural network by passing your training data through the network and adjusting the weights using backpropagation. You'll also need to define a loss function that measures the difference between the network's output and the expected output. Here's an example of how to train your neural network:
```python
net = Net()   # create an instance of the network
criterion = nn.MSELoss()   # define the loss function
optimizer = torch.optim.SGD(net.parameters(), lr=0.01)   # define the optimizer

for epoch in range(100):
    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 % 10 == 9:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 10))
            running_loss = 0.0
```
This code will train your network for 100 epochs using stochastic gradient descent (SGD) as the optimizer and mean squared error (MSE) as the loss function.

8. Test your neural network: Once your network is trained, test it using your testing data
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