Research - A Gentle Introduction to Deep Learning with Python and R
Machine learning methods are nowadays used in a wide variety of applications such as image and text classification, and weather forecasting. In this course, you will learn how a very popular method, namely Deep Learning (or Neural Network), works and may be applied in practice by using either Python or R programming.
Objectives
Acquire the key competencies that are needed to apply deep learning methods to simple datasets
Target audience
Any PhD students, post-docs, researchers of UNIL who would like to use deep learning methods in their research
Content
At the end of the course, the participants are expected to:
Understand how the deep learning (neural network) algorithm works
Run a simple machine learning code in Python or R
Be able to choose properly the hyperparameters of the model
Length
1 half-day
Organization
Every 6 months
Location
To be defined or remotely
Practicals
The practicals can be done on your laptop (but you will need to install the required libraries), on the EPFL JupyterLab, or on the UNIL cluster called Curnagl. See the installation page for more information.
Prerequisites
Basic knowledge of statistics, including simple linear algebra techniques such as vectors, matrices and matrix multiplication
Be confortable with either Python or R programming
IMPORTANT: Please register using your UNIL email address!
Course dates and registration