Introduction to Computer Vision with PyTorch

Module
8 Units

Beginner

Data Scientist

Developer

Student

Azure

Azure 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

Prerequisites

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.

Introduction min
Introduction to processing image data min
Training a simple dense neural network min
Use a convolutional neural network min
Train multi-layer convolutional neural network min
Use a pre-trained network with transfer learning min
Solving vision problems with MobileNet min
Summary min