Lesson 32 of 40 AI / ML Expert ⏱ 35 min

Deep Learning with PyTorch

Train neural networks with PyTorch — tensors, autograd, nn.Module, DataLoader, GPU training, transfer learning, and ONNX export.

Part 1: Introduction to Deep Learning with PyTorch

Train neural networks with PyTorch — tensors, autograd, nn.Module, DataLoader, GPU training, transfer learning, and ONNX export.

This lesson uses Python 3.13 features and follows best practices for development in Visual Studio 2026 with Copilot assistance.

Part 2: Core Concepts & Code Examples

# Deep Learning with PyTorch — Python 3.13 Example
from typing import Any

def main() -> None:
    """Entry point demonstrating lesson 32 concepts."""
    print(f"Lesson 32: Deep Learning with PyTorch")

if __name__ == "__main__":
    main()

Part 3: Best Practices & Patterns

Apply the patterns from this lesson consistently across your projects. Visual Studio 2026's Python IntelliSense, type checking integration, and GitHub Copilot will guide you toward idiomatic, production-ready Python 3.13 code.

Use type hints for all function signatures
Write docstrings with Args/Returns sections
Run ruff for linting, mypy for type checking
Test every function with at least one pytest test

Part 4: Next Steps

Practice these concepts hands-on, then continue to Lesson 33. Return to Python Tutorial Home to see the full curriculum, or visit VisualStudioTutor.com for Visual Studio 2026 guides.