Step by Step Tutorial: Building a Simple Neural Network with PyTorch in 5 Minutes!

If you are new to machine learning or neural networks, PyTorch is a great library to start with. In this tutorial, we will walk you through building a simple neural network using PyTorch in just 5 minutes!

Step 1: Install PyTorch

Before we get started, make sure you have PyTorch installed on your system. You can install PyTorch using pip:

pip install torch

Step 2: Import PyTorch

Now, let’s import PyTorch in your code:

import torch

Step 3: Create a Simple Neural Network

Next, let’s create a simple neural network with 1 input layer, 1 hidden layer, and 1 output layer:

model = torch.nn.Sequential(
torch.nn.Linear(1, 10),
torch.nn.ReLU(),
torch.nn.Linear(10, 1)
)

Step 4: Define Loss Function and Optimizer

Now, let’s define the loss function and optimizer for training our neural network:

criterion = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

Step 5: Train the Neural Network

Finally, let’s train our neural network with some dummy data:

for epoch in range(100):
output = model(input_data)
loss = criterion(output, target)

optimizer.zero_grad()
loss.backward()
optimizer.step()

And that’s it! You have successfully built a simple neural network using PyTorch in just 5 minutes. Feel free to experiment with different architectures and datasets to enhance your understanding of neural networks.