Please note: This instance is for testing/development, and any content submitted may be changed or deleted without warning.
Training eSupport System
  • Log In
    • Login
    • Register
  • About
  • Events
  • Materials
  • e-Learning
  • Workflows
  • Collections
  • Learning paths
  • Directory
    • Trainers
    • Providers
    • Nodes

TeSS makes use of some necessary cookies to provide its core functionality.

See our Privacy Policy for more information.

You can modify your cookie preferences at any time here, or from the link in the footer.

Allow necessary cookies
  1. Home
  2. Materials

Filter

  • Sort

  • Filter Clear filters

    • Scientific topic
    • Probabilistic graphical model
    • Bayesian methods6
    • Biostatistics6
    • Descriptive statistics6
    • Gaussian processes6
    • Inferential statistics6
    • Markov processes6
    • Multivariate statistics6
    • Probability6
    • Statistics6
    • Statistics and probability6
    • Show N_FILTERS more
    • Tool
    • Galaxy6
    • scikit-learn4
    • Show N_FILTERS more
    • Content provider
    • Galaxy Training6
    • Show N_FILTERS more
    • Keyword
    • Statistics and machine learning6
    • deep-learning2
    • interactive-tools2
    • jupyter-lab2
    • machine-learning2
    • dephosphorylation-site-prediction1
    • fine-tuning1
    • image-segmentation1
    • protein-3D-structure1
    • Show N_FILTERS more
    • Difficulty level
    • Beginner6
    • Show N_FILTERS more
    • Licence
    • Creative Commons Attribution 4.0 International6
    • Show N_FILTERS more
    • Target audience
    • Students6
    • Show N_FILTERS more
    • Author
    • Anup Kumar
    • Kaivan Kamali6
    • Alireza Khanteymoori2
    • Daniel Blankenberg2
    • Fabio Cumbo2
    • Simon Bray2
    • Bérénice Batut1
    • Dennis Lal group1
    • Fotis E. Psomopoulos1
    • Marie Gramm1
    • Marzia A Cremona1
    • Vijay1
    • Show N_FILTERS more
    • Contributor
    • Helena Rasche
    • Armin Dadras11
    • Björn Grüning11
    • Martin Čech11
    • Saskia Hiltemann9
    • Anup Kumar6
    • Teresa Müller6
    • Alireza Khanteymoori5
    • Bérénice Batut3
    • Fabio Cumbo1
    • Gildas Le Corguillé1
    • Kaivan Kamali1
    • Michelle Terese Savage1
    • Mélanie Petera1
    • Simon Bray1
    • Show N_FILTERS more
    • Resource type
    • e-learning6
    • Show N_FILTERS more
    • Related resource
    • Associated Training Datasets6
    • Associated Workflows5
    • Show N_FILTERS more
  • Show disabled materials
  • Show archived materials
    • Date added
    • In the last 24 hours
    • In the last 1 week
    • In the last 1 month

Training materials

  • Subscribe via email

Email Subscription

Register training material

Scientific topics: Probabilistic graphical model

and Authors: Anup Kumar

and Contributors: Helena Rasche

6 materials found
  • e-learning

    Fine tune large protein model (ProtTrans) using HuggingFace

    • beginner
    Statistics and probability Statistics and machine learning deep-learning dephosphorylation-site-prediction fine-tuning interactive-tools jupyter-lab machine-learning
  • e-learning

    A Docker-based interactive Jupyterlab powered by GPU for artificial intelligence in Galaxy

    • beginner
    Statistics and probability Statistics and machine learning deep-learning image-segmentation interactive-tools jupyter-lab machine-learning protein-3D-structure
  • e-learning

    Machine learning: classification and regression

    • beginner
    Statistics and probability Statistics and machine learning
  • e-learning

    Regression in Machine Learning

    • beginner
    Statistics and probability Statistics and machine learning
  • e-learning

    Basics of machine learning

    • beginner
    Statistics and probability Statistics and machine learning
  • e-learning

    Classification in Machine Learning

    • beginner
    Statistics and probability Statistics and machine learning
Training eSupport System
contact@example.com
Contribute
About TeSS
Funding & acknowledgements
Privacy
Cookie preferences
Version: 1.5.0
Source code
API documentation
Bioschemas testing tool

TeSS has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 676559.