Diving into Deep Learning - Theory and Applications with PyTorch

Overview

With the rise of new technologies, the volume of omics data in biology and medicine has grown exponentially recently. A major issue is to mine useful predictive knowledge from these data. Machine learning (ML) is a discipline in which computer algorithms perform automated learning by using data to assist humans in dealing with a large volume of multidimensional data, and deep learning is one of these methods. Deep learning is based on artificial neural networks inspired by the structure and function of the human brain. It has been widely applied in computer vision, natural language processing, computational biology, etc.

This course aims to give the participants some practical knowledge of deep learning models in life sciences. This course will not make the participant an absolute expert in the complex and dynamic world of Deep-Learning. Still, it will aim to “break the ice” through the explanation and implementation of simple yet concrete, deep-learning models using the PyTorch library. Participants will be introduced to the basic building blocks of deep-learning models and how the main parameters are tuned and monitored to ensure the training of large models.

Audience

This course is designed for PhD students, postdoctoral and other researchers in the life sciences from both academia and industry who already know about Machine Learning and would like to discover and start practising Deep Learning with PyTorch.

Learning outcomes

At the end of the course, the participants should be able to:

• Create simple deep-learning models
• Identify deep learning parameters
• Train and evaluate a deep-learning auto-encoder model
• Adapt a pre-existing deep-learning model to a new task using fine-tuning

Prerequisites

Knowledge / competencies required

• Prior knowledge of ML concepts and methods is required.
• A good fluency with the Python programming language, including working knowledge of common data analysis libraries such as numpy, panda, matplotlib or scikit-learn.
• Familiarity with different omics data technologies (highly recommended).

This course is part of the Machine Learning learning path. To get the most out of this course, you should meet the learning outcomes of the Introduction to Machine Learning with Python, the First Steps with Python in Life Sciences and the Introduction to Statistics and Data Visualisation with R.

Technical

You will need access to a computer with WI-FI enabled. The needed libraries are indicated in the dedicated page on the GitHub repo.

Application

Registration fees are 200 CHF for academics and 1000 CHF for for-profit companies.

While participants are registered on a first come, first served basis, exceptions may be made to ensure diversity and equity, which may increase the time before your registration is confirmed.

Applications will close on 11/11/2026 or as soon as the places will be filled up. Cancellation after 11/11/2026 will not be reimbursed. Please note that participation in SIB courses is subject to our general conditions.

You will be informed by email of your registration confirmation. Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 working days.

Venue and Time

This course will be streamed.

The course will start at 9:00 CET and end around 17:00 CET.

Precise information will be provided to the participants in due time.

Additional information

Coordination: Diana Marek, SIB Training Group.

A Certificate of Attendance will be sent provided you were present at the course, whereas a Certificate of Achievement recommending 0.5 ECTS will be sent provided you passed the exam.

You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.

SIB abides by the ELIXIR Code of Conduct. Participants of SIB courses are also required to abide by the same code.

For more information, please contact training@sib.swiss.