PyTorch Crash Course

Course Overview

• Introduction to PyTorch framework
• Teaching through practical coding of a simple neural network
• Focus on understanding underlying basics
• Prerequisites: Decent Python skills
• Deep learning concepts like backpropagation are not covered in detail
• Recommended resources: Deep Learning Explained series

Course Outline

1. Tensor Basics
2. Autograd in PyTorch for Gradient Computation
3. Training Loop with PyTorch Model, Loss, and Optimizer
4. Building a Simple Neural Network
5. Building a Convolutional Neural Network (CNN)

Installation

• Install PyTorch from pytorch.org
• Choose configuration: OS, package manager, CUDA version
• Options: Use Google Colab for free GPU access

Chapter 1: Tensor Basics

• Tensors: Multi-dimensional matrices with GPU support
• Creation functions: torch.empty, torch.rand, torch.zeros, torch.ones
• Accessing tensor size: x.size() or x.shape
• Data type specification: dtype
• Convert from list or numpy array using torch.tensor
• requires_grad: Used for optimization steps
• Tensor operations: Addition, subtraction, multiplication, division
• Slicing and reshaping tensors
• Conversion between PyTorch tensor and numpy array
• GPU support: Checking availability and moving tensors

Chapter 2: Autograd

• Automatic differentiation engine
• Importance in gradient computation for training
• Computational graph: Tracks operations
• Gradient calculation with requires_grad=True
• Use detach() or torch.no_grad() to prevent tracking
• Linear regression example using autograd

Chapter 3: Model, Loss, and Optimizer

• Typical PyTorch pipeline for models
• Use of built-in loss functions and optimizers
• Linear regression example revisited with PyTorch methods
• Training loop: Forward pass, loss calculation, backward pass, optimizer step

Chapter 4: Building a Neural Network

• Designing a model with neural network layers
• Leveraging GPU, datasets, and dataloaders
• Using transforms and evaluating the model post-training
• Example using MNIST dataset with simple neural network

Chapter 5: Building a CNN

• Introduction to convolutional and pooling layers
• Using CIFAR-10 dataset
• Defining a CNN model architecture
• Training and evaluating the model
• Saving and loading model states with torch.save and torch.load

Additional Resources

• Deep Learning Explained series on YouTube
• Extended PyTorch course available

Conclusion

• Encouragement to explore further and improve models
• Reminder of additional resources for deeper learning