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DTSTAMP:20260716T160424Z
UID:64a212e5-efe0-46c9-9c53-6f001f56e3f3
DTSTART:20261203T090000Z
DTEND:20261204T170000Z
DESCRIPTION:## Overview\n \nWith 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. Machin
 e learning (ML) is a discipline in which computer algorithms perform autom
 ated learning by using data to assist humans in dealing with a large volum
 e of multidimensional data\, and deep learning is one of these methods. De
 ep learning is based on artificial neural networks inspired by the structu
 re and function of the human brain. It has been widely applied in computer
  vision\, natural language processing\, computational biology\, etc.\n\nTh
 is course aims to give the participants some practical knowledge of deep l
 earning models in life sciences. This course will not make the participant
  an absolute expert in the complex and dynamic world of Deep-Learning. Sti
 ll\, it will aim to “break the ice” through the explanation and implem
 entation of simple yet concrete\, deep-learning models using the PyTorch l
 ibrary. Participants will be introduced to the basic building blocks of de
 ep-learning models and how the main parameters are tuned and monitored to 
 ensure the training of large models.\n\n\n## Audience\nThis course is desi
 gned for PhD students\, postdoctoral and other researchers in the life sci
 ences from both academia and industry who already know about Machine Learn
 ing and would like to discover and start practising Deep Learning with PyT
 orch.\n\n## Learning outcomes\nAt the end of the course\, the participants
  should be able to:\n* **Create** simple deep-learning models\n* **Identif
 y** deep learning parameters\n* **Train** and **evaluate** a deep-learning
  auto-encoder model \n* **Adapt** a pre-existing deep-learning model to a 
 new task using fine-tuning\n\n\n## Prerequisites\n###  Knowledge / compete
 ncies required\n\n* Prior knowledge of ML concepts and methods is required
 .\n* A good fluency with the Python programming language\, including worki
 ng knowledge of common data analysis libraries such as numpy\, panda\, mat
 plotlib or scikit-learn.\n* Familiarity with different omics data technolo
 gies (highly recommended).\n\nThis course is part of the [Machine Learning
 ](https://www.sib.swiss/training/learning-paths?path=machine-learning) lea
 rning path. To get the most out of this course\, you should meet the learn
 ing outcomes of the [Introduction to Machine Learning with Python](https:/
 /www.sib.swiss/training/course/INMLP)\, the [First Steps with Python in Li
 fe Sciences](https://www.sib.swiss/training/course/20260928_FSWPY) and the
  [Introduction to Statistics and Data Visualisation with R](https://www.si
 b.swiss/training/course/20260126_STATR).\n\n\n###  Technical\n  \nYou will
  need access to a computer with WI-FI enabled.\nThe needed libraries are i
 ndicated in the [dedicated page on the GitHub repo](\nhttps://github.com/s
 ib-swiss/pytorch-practical-training).\n\n\n\n## Application\n\nRegistratio
 n fees are **200 CHF** for academics and **1000 CHF** for for-profit compa
 nies. \n\nWhile 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. \n\nApplications
  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](https
 ://www.sib.swiss/legal-documents).\n\nYou will be informed by email of you
 r registration confirmation. Upon reception of the confirmation email\, pa
 rticipants will be asked to confirm attendance by paying the fees within *
 *5 working days**.\n\n\n## Venue and Time\nThis course will be streamed. \
 n\nThe course will start at 9:00 CET and end around 17:00 CET.\n\nPrecise 
 information will be provided to the participants in due time.\n\n\n##  Add
 itional information\nCoordination: Diana Marek\, SIB Training Group.\n\nA 
 **Certificate of Attendance** will be sent provided you were present at th
 e course\, whereas a **Certificate of Achievement** recommending 0.5 ECTS 
 will be sent provided you passed the exam.\n\nYou are welcome to register 
 to the SIB courses mailing list to be informed of all future courses and w
 orkshops\, as well as all important deadlines using the form [here](https:
 //lists.sib.swiss/mailman/listinfo/courses).\n\nSIB abides by the [ELIXIR 
 Code of Conduct](https://elixir-europe.org/events/code-of-conduct). Partic
 ipants of SIB courses are also required to abide by the same code.\n\nFor 
 more information\, please contact [training@sib.swiss](mailto://training@s
 ib.swiss).
SUMMARY:Diving into Deep Learning - Theory and Applications with PyTorch
URL;VALUE=URI:https://www.sib.swiss/training/course/20261203_DEEPP
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