PyTorch Basics Summary

Overview

This page summarizes key concepts from the PyTorch basics tutorial series. Each topic below links to a dedicated page with code and examples to help you practice and deepen your understanding.

Key Concepts

• Introduction to PyTorch: Why use PyTorch, installation, and comparison with TensorFlow.
• Tensors in PyTorch: Creating, reshaping, and operating on tensors including GPU usage.
• Autograd & Gradient Calculation: Using requires_grad, backward(), and understanding the computation graph.
• Building Models with nn.Module: Defining and organizing layers, forward methods, and model parameters.
• Training Loop: Setting up datasets, DataLoader, loss function, optimizer, and updating model weights.

What’s Next?

• Work with real-world datasets using torchvision.
• Try classification tasks like MNIST or CIFAR-10.
• Explore transfer learning and pre-trained models.
• Deploy your model using Flask or FastAPI for inference.

Practice Challenge

Create a linear regression model from scratch using synthetic data, train it using your own training loop, and visualize the loss curve.

Extended Key Concepts with Examples

• PyTorch Overview: Learn why PyTorch is widely used in both research and production. Includes comparison with TensorFlow and how dynamic graphs improve flexibility.
• Working with Tensors: Master tensor creation, reshaping, indexing, broadcasting, and GPU transfers.
• Autograd & Gradients: Explore automatic differentiation, computation graphs, and how to manage gradients with requires_grad and backward().
• Building Models with nn.Module: Structure models using custom classes, encapsulate layers, and forward logic for better maintainability.
• Training Loops: Setup training from scratch with DataLoader, loss functions, and optimizers. Perform model saving and loading.

📦 Additional Concepts You Can Explore

• Data Preprocessing: Use torchvision.transforms to normalize, augment, or convert images.
• Custom Datasets: Extend torch.utils.data.Dataset to create your own data loaders for CSVs or image folders.
• Model Evaluation: Add validation loops and accuracy/loss monitoring to measure model performance.
• Loss Curves: Use matplotlib to visualize training and validation loss over time.

🧠 What’s Next? Try These Mini Projects

• Linear Regression Challenge: Generate random X values, calculate y = mx + b, add noise, and train a model to learn the line.
• MNIST Digit Classification: Use torchvision.datasets.MNIST to build a digit classifier.
• Transfer Learning: Load pre-trained models like resnet18, freeze layers, and fine-tune the final layer.
• API Deployment: Package your model using Flask or FastAPI for real-time inference on web input.

📌 Sample Practice Task

import torch
import torch.nn as nn
import matplotlib.pyplot as plt

# Synthetic Data
X = torch.linspace(0, 10, 100).unsqueeze(1)
y = 2 * X + 3 + torch.randn(X.size()) * 0.5

# Model
model = nn.Linear(1, 1)
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

losses = []
for epoch in range(100):
    pred = model(X)
    loss = criterion(pred, y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    losses.append(loss.item())

# Plot loss curve
plt.plot(losses)
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title("Training Loss Curve")
plt.show()

• Creates a noisy linear dataset and trains a simple regression model.
• Plots the training loss curve to visualize model learning progress.

🔚 Conclusion

With these core PyTorch skills, you’re ready to build, train, evaluate, and deploy your own models. Keep revisiting each module and challenge yourself with real-world problems. The more you experiment, the more you’ll solidify your understanding.