e-learning

Deep Learning (Part 3) - Convolutional neural networks (CNN)

View material

Abstract

Artificial neural networks are a machine learning discipline that have been successfully applied to problems

About This Material

This is a Hands-on Tutorial from the GTN which is usable either for individual self-study, or as a teaching material in a classroom.

Questions this will address

  • What is a convolutional neural network (CNN)?
  • What are some applications of CNN?

Learning Objectives

  • Understand the inspiration behind CNN and learn the CNN architecture
  • Learn the convolution operation and its parameters
  • Learn how to create a CNN using Galaxy's deep learning tools
  • Solve an image classification problem on MNIST digit classification dataset using CNN in Galaxy

Licence: Creative Commons Attribution 4.0 International

Keywords: Statistics and machine learning

Competency level: • Beginner

Target audience: Students

Resource type: e-learning

Version: 20

Status: Active

Prerequisites:

  • Deep Learning (Part 1) - Feedforward neural networks (FNN)
  • Deep Learning (Part 2) - Recurrent neural networks (RNN)
  • Feedforward neural networks (FNN) Deep Learning - Part 1
  • Introduction to deep learning
  • Recurrent neural networks (RNN) Deep Learning - Part 2

Learning objectives:

  • Understand the inspiration behind CNN and learn the CNN architecture
  • Learn the convolution operation and its parameters
  • Learn how to create a CNN using Galaxy's deep learning tools
  • Solve an image classification problem on MNIST digit classification dataset using CNN in Galaxy

Date modified: 2026-05-11

Date published: 2021-04-19

Authors: Kaivan Kamali

Contributors: Kaivan Kamali, qiagu, Anup Kumar, Björn Grüning, Helena Rasche, Martin Čech, Michelle Terese Savage, Saskia Hiltemann, Teresa Müller

Scientific topics: Statistics and probability

Activity log