This PyTorch crash course provides a concise introduction to the framework, covering everything from basic tensor operations to building and training a neural network.
Core Concepts and Environment
Introduction to PyTorch: Overview of why PyTorch is a leading deep learning framework and its two main components: a GPU-accelerated replacement for NumPy and an automatic differentiation engine.
Environment Setup: Instructions for installing PyTorch (torch) and necessary libraries like Jupyter Lab, NumPy, and Scikit-Learn using pip or uv.
Hardware and CUDA: How to check for CUDA availability, count GPU devices, and retrieve device names to ensure your hardware is ready for acceleration.
Low-Level Operations
Tensors vs. NumPy: Demonstrating how PyTorch tensors mirror NumPy arrays but add the ability to run on GPUs for parallel computation.
GPU Acceleration: Moving tensors between CPU and GPU memory and setting up device-agnostic code.
Automatic Differentiation: A deep dive into autograd, showing how PyTorch automatically calculates gradients for backpropagation using .backward().
Building a Neural Network
Data Preparation: Loading the Breast Cancer dataset, scaling features with StandardScaler, and converting data into PyTorch TensorDataset and DataLoader.
Defining the Model: Creating a custom neural network class by inheriting from nn.Module and defining linear layers and activation functions.
Loss Functions and Optimizers: Setting up Binary Cross Entropy (BCE) loss and the Adam optimizer to update model weights.
Training and Evaluation
The Training Loop: Implementing the manual training process, including zeroing gradients, performing forward passes, backpropagating loss, and stepping the optimizer.
Model Evaluation: Testing the trained model on unseen data using torch.no_grad() to calculate final accuracy.