Computer Vision with PyTorch: Practical Deep Learning

About this course

Build your first computer vision model. Then learn how to make it better.

Turn your Python skills into working image classification projects with PyTorch. Learn how to train a neural network, understand its predictions, and make informed decisions about what to improve next.

Whether you are building your first model or have already followed a few tutorials, this course gives you a connected path through the complete workflow: prepare your images, build a model, train it, evaluate its performance, and experiment with improvements.

See your first classifier in action early in the course

You will begin with a guided Rock-Paper-Scissors project, using a provided notebook to see how the pieces fit together. You can follow the complete process before learning every implementation detail.

From there, we will explore how neural networks learn and connect those ideas to practical PyTorch code. You will develop an intuitive understanding of predictions, loss, backpropagation, and parameter updates, then see how convolutional neural networks work with images.

Build one project and improve it step by step

The main project uses the Intel Image Classification dataset to recognize six types of scenes, including buildings, forests, and mountains.

You will start with a simple CNN and follow its development across several modules. As you add new techniques, you will compare results and investigate what changed.

Along the way, you will learn to:

• Prepare image datasets, transformations, and data loaders.
• Build and train a convolutional neural network in PyTorch.
• Use training, validation, and test sets for their different purposes.
• Interpret learning curves, classification metrics, and confusion matrices.
• Apply transfer learning with a pretrained ResNet-18.
• Fine-tune selected layers to adapt the model to your task.

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