PyTorch: Fundamentals

Overview

This course introduces you to the core principles of deep learning through hands-on coding in PyTorch. You’ll start by learning how PyTorch represents data with tensors and how datasets and data loaders fit into the training process. Step by step, you’ll build and train neural networks, experiment with different architectures, and explore how models learn from examples. You’ll also learn how to monitor training progress, interpret results, and evaluate performance. By the end of the course, you’ll understand PyTorch’s workflow and be ready to design, train, and test your own neural networks with confidence.

Syllabus

Getting Started with PyTorch

In this module, you’ll get started with PyTorch, the framework that revolutionized deep learning by making it as intuitive as writing Python code. You’ll progress from a single neuron that models linear relationships to multi-neuron networks with activation functions for complex patterns. Along the way, you’ll build and train your first models, learn how to work with tensors, and see the complete machine learning pipeline in action.

The PyTorch Workflow

In this module, you’ll move from regression to image classification, tackling the challenges of working with image data. You’ll learn to manage datasets with PyTorch’s transforms, Dataset, and DataLoader, and to build models beyond Sequential using nn.Module. Along the way, you’ll see how networks learn through loss functions, gradients, and optimization, apply GPU acceleration, and put it all together by training classifiers for digits and letters end to end.

Data Management in PyTorch

This module tackles real-world data challenges with the Oxford Flowers dataset, showing how poor pipelines can break even the best models. You’ll learn to build custom Datasets, implement transform pipelines, split data correctly, and apply production-ready practices like error handling, augmentation, and monitoring to create a reliable workflow.

Core Neural Network Components

In this module, you’ll explore Convolutional Neural Networks (CNNs), learning how filters detect patterns like edges and textures, pooling reduces dimensions, and these components combine into full architectures. You’ll see how PyTorch’s dynamic graphs let you choose between quick Sequential models and flexible custom modules. By the end, you’ll build CNNs with dropout, weight decay, and inspection tools to debug shape mismatches and understand parameters.