Introduction to Computer Vision with PyTorch

Data Scientist
Machine Learning

We'll learn about different computer vision tasks and focus on image classification, learning how to use neural networks to classify handwritten digits, as well as some real-world images, such as photographs of cats and dogs. We'll be using one of the most popular deep learning frameworks, PyTorch!

Learning objectives

In this module you will:

  • Learn about computer vision tasks most commonly solved with neural networks
  • Understand how Convolutional Neural Networks (CNNs) work
  • Train a neural network to recognize handwritten digits and classify cats and dogs.
  • Learn how to use Transfer Learning to solve real-world classification problems with PyTorch


  • Basic knowledge of Python and Jupyter Notebooks
  • Familiarity with PyTorch framework, including tensors, basics of back propagation and building models
  • Understanding machine learning concepts, such as classification, train/test dataset, accuracy, etc.