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Event types: Workshops and courses 

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  • Dresden Deep Learning Hackathon

    9 - 13 September 2019

    Dresden, Germany

    Dresden Deep Learning Hackathon https://tess.elixir-europe.org/events/dresden-deep-learning-hackathon Educators: Florian Jug, Peter Steinbach, others (DAIS/CIBI) Date: 09.09.2019 – 13.09.2019 Location: SLUB, Klemperer-Saal Zellescher Weg 18 01069 Dresden Germany Contents: The Dresden Deep Learning Hackathon ( #d3hack2019 ) is meant to bring together machine learning experts and scientific practitioners. Teams of 2-4 scientists can apply for the hackathon given a scientific problem they want to solve with machine learning. Upon approval, they will be assisted by one or two machine learning experts for 5 days consecutively! This effort is meant to give your team a head-start and potentially create an end-to-end machine learning solution for your science. The teams are motivated to publish a scientific paper about the hackathon efforts at dedicated conferences or in established journals - at best jointly with their mentors - after the hackathon. A win-win situation for all parties involved. The scope of scientific domains that can apply is not limited. For sure, our mentors have a given background mostly with regard to 2D or 3D images. So we will try to match that as close as possible. However, we are still in the process of fixing mentors (we have expressions of interest of about 5 more than listed below). We will also consider a limited amount of applications using standard machine learning (MLP, SVM, RandomForests,...). If you are unclear whether your topic fits the hackathon, please reach out to us. Most importantly, any team without a readily available data set for training will be discarded from the candidate list. In other words, if you are interested in applying machine learning to your data, you shouldn't use the hackathon to annotate your data. The workshop admission fee amounts to € 300 per participant to cover room rent and catering. We are still looking for sponsors, so there is a non-negligible probability that the admission fee will be reduced in the future. The call for applications closes on June 30, 2019, at 12pm AoE! After this date, a review board of mentors and organizers will judge the applications and send out confirmations to the applications until mid July the latest. The registration mechanism of participants will be circulated then. For members of non-academic institutions: We cannot allow applications from non-academic institutions or industry to our hackathon. If you want to participate with a project as a company, this project needs to be embedded in a scientific group and the majority of team members need to be employed by a scientific institution. On top, the results of the hackathon are expected to be published. So be prepared to undisclose your results and (at best) the data and code which produced these results. Learning goals: - Fundamentals of deep learning with CNNs. - Keras API with the Tensorflow backend. - How to define your deep net. - How to train it. Prerequisites: Bring your own laptop. Keras and Tensorflow backend should already be installed. (We will have GPU nodes you can use if your laptop does not offer a fast GPU.) Solid understanding of the fundamentals of linear algebra. Programming skills (never programmed… that will not work out, sorry!) Keywords: DeepLearning, Keras, Tensorflow, Python Tools: Keras, Python, Tensorflow 2019-09-09 09:00:00 UTC 2019-09-13 17:00:00 UTC de.NBI Dresden, Dresden, Germany Dresden Dresden Dresden Germany [] [] [] workshops_and_courses [] []
  • Software Carpentry Workshop

    16 - 18 October 2019

    Heidelberg, Germany

    Software Carpentry Workshop https://tess.elixir-europe.org/events/software-carpentry-workshop-ab3af408-aa91-49ed-bab2-5db1f2e6d15d Educators: Malvika Sharan, Georg Zeller, Mike Smith, Thomas Schwarzl, Frank Thommen (HD-HuB), Holger Dinkel Date: 16-10-2019 - 18-10-2019 09:00-18:00 Location: ATC Computer Training Lab, EMBL Heidelberg Contents: Computation is an integral part of today's research as data has grown too large or too complex to be analysed by hand. An ever-growing fraction of science is performed computationally and many wet-lab biologists spend part of their time on the computer. Many scientists struggle with this aspect of research as they have not been properly trained in the necessary set of skills. The result is that too much time is spent using inefficient tools when progress could be faster. This course provides training in several key tools, with a focus on good development practices that encourage efficient and reproducible research computing. Topics covered include: Introduction to Python scripting Introduction to the Unix shell and usage of cluster resources Version control with Git and Github Analysis pipeline management Scientific Python & working with biological data Literate programming with Jupyter notebooks Learning goals: This course aims to teach software writing skills and best practices to researchers in biology who wish to analyse data, and to introduce a toolset that can help them in their work. The goal is to enable them to be more productive and to make their science better and more reproducible. Prerequisites: This is a course for researchers in the life sciences who are using computers for their analyses, even if not full time. The target student will be familiar with some command line/programmatic computer usage, will want to become more confident using these tools efficiently and reproducibly. A target student will have written a for loop in some language before, but will not know what git is (or at least not be very comfortable using git). Keywords: Programming; Command Line; Version Control; Bioinformatics; Data Analysis; Cluster Computing Tools: Python; Bash; Unix/Linux; Git; GitHub; SnakeMake; Biopython; Pandas; Numpy; SciPy; Matplotlib 2019-10-16 09:00:00 UTC 2019-10-18 17:00:00 UTC de.NBI / ELIXIR Heidelberg, Heidelberg, Germany Heidelberg Heidelberg Karlsruhe Germany [] [] [] workshops_and_courses [] []
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