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Overview Suppose that you have finished writing your code, say a python code called <my_code.py>, and you want to run it on the cluster Curnagl. You will need to submit a job (a bash script) with information such as the number of CPU you want to use and the a...

The training phase of your deep learning model may be very time consuming. To accelerate this process you may want to use GPUs and you will need to install the deep learning packages, such as Keras or PyTorch, properly. Here is a short documentation on how to ...

Machine learning methods are nowadays used in a wide variety of applications. In this course, you will learn how the decision tree and random forest methods work and may be applied in practice by using either Python or R programming. Objectives Acquire the k...

Convolutional Neural Networks (CNNs) are used in a wide variety of applications such as image classification, image segmentation, object detection, and image generation (with GAN). In this course, you will learn how a CNN works and how it can be applied in pra...

You can do the practicals on various computing platforms. However, since the participants may use various types of computers and softwares, we recommend to use the UNIL JupyterLab to do the practicals.  JupyterLab: Working on the cloud is convenient because t...