Research - A Gentle Introduction to Image Analysis with CNNs in Python

MachineConvolutional learningNeural methodsNetworks (CNNs) are nowadays used in a wide variety of applications such as image andclassification, textimage classification,segmentation, object detection, and weatherimage forecasting.generation (with GAN). In this course, you will learn how a very popular method, namely Deep Learning (or Neural Network),CNN works and mayhow it can be applied in practice in image classification and image segmentation by using either Python or R programming.

Objectives

Acquire the key competencies that are needed to apply deep learningCNN methods to simpledo datasetsimage classification and image segmentation

Target audience

Any PhD students, post-docs, researchers of UNIL who would like to use deep learning methodsCNNs in their research

Content

At the end of the course, the participants are expected to:

• Understand how theCNNs deep learning (neural network) algorithm works
• Run a simple machine learning code in Python or Rwork
• Be able to chooseuse properlyCNNs theto hyperparametersdo ofimage theclassification modeland image segmentation in Python
  

Length

1 half-day

Organization

Once per year

Location

In presential

Practicals

Prerequisites

• Basic knowledge of statistics,deep learning: we assume that you know how simple feedforward neural networks work, including simplehow linearto algebrainterpret techniques such as vectors, matricesaccuracy and matrixloss multiplicationcurves (for example by attending the course "A Gentle Introduction to Deep Learning with Python and R").
• Be confortable with either Python or R programming

IMPORTANT: Please register using your UNIL email address!

Course dates and registration