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1. Introduction to PyTorch

PyTorch is widely used for its flexibility and ease of use, especially in research and development. It provides two high-level features:

• Tensors: Similar to NumPy arrays, but with GPU acceleration.
• Autograd: An automatic differentiation engine that supports all differentiable routines.

Let's start by installing PyTorch. Visit the official PyTorch website for installation instructions tailored to your system.

2. Tensors in PyTorch

Tensors are the fundamental data structures in PyTorch. They are N-dimensional arrays.

import torch

# Create a tensor
x = torch.tensor([[1, 2], [3, 4]])
print(x)

# Tensor operations
y = x + 2
print(y)

z = x * y
print(z)

# Tensor with GPU acceleration (if available)
if torch.cuda.is_available():
    device = torch.device("cuda")
    x_gpu = x.to(device)
    print(x_gpu)
            

3. Building a Simple Neural Network

We'll use PyTorch's torch.nn module to define neural network layers.

import torch
import torch.nn as nn
import torch.optim as optim

# Define a simple neural network
class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(10, 50) # Input layer (10 features) to hidden layer (50 neurons)
        self.relu = nn.ReLU()       # Activation function
        self.fc2 = nn.Linear(50, 2)  # Hidden layer (50 neurons) to output layer (2 classes)

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

# Instantiate the network
model = SimpleNN()
print(model)
            

4. Training the Model

Training involves defining a loss function and an optimizer.

# Dummy data for demonstration
inputs = torch.randn(64, 10) # Batch size 64, 10 features
labels = torch.randint(0, 2, (64,)) # 64 samples, 2 classes

# Loss function (e.g., CrossEntropyLoss for classification)
criterion = nn.CrossEntropyLoss()

# Optimizer (e.g., Adam)
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Training loop (simplified)
num_epochs = 10
for epoch in range(num_epochs):
    # Forward pass
    outputs = model(inputs)
    loss = criterion(outputs, labels)

    # Backward pass and optimization
    optimizer.zero_grad() # Clear gradients
    loss.backward()       # Compute gradients
    optimizer.step()      # Update weights

    if (epoch+1) % 2 == 0:
        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')
            

This is a basic example. Real-world training involves more complex data loading, validation, and hyperparameter tuning.

5. Next Steps

Now that you have a grasp of the basics, explore more advanced topics:

• Convolutional Neural Networks (CNNs) for image data.
• Recurrent Neural Networks (RNNs) for sequential data.
• Transfer Learning and Pre-trained Models.
• Deployment strategies for your models.

Explore Advanced PyTorch Topics