Training eSupport System
  • Log In
    • Log in with LS Login
    • Login
    • Register
  • About
  • Events
  • Materials
  • e-Learning
  • Workflows
  • Collections
  • Learning paths
  • Directory
    • Providers
    • Nodes
    • Spaces

TeSS makes use of some necessary cookies to provide its core functionality. Additionally, we make use of Google Analytics to discover how people are using TeSS in order to help us improve the service. To opt out of this, choose the "Allow necessary cookies" option.

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 Allow all cookies
  1. Home
  2. Materials

Filter

  • Sort

  • Filter Clear filters

    • Date added
    • In the last 24 hours
    • In the last 1 week
    • In the last 1 month
    • Scientific topic
    • Bayesian methods
    • Algorithms53
    • Computer programming53
    • Data structures53
    • Programming languages53
    • Software development53
    • Software engineering53
    • Exomes51
    • Genome annotation51
    • Genomes51
    • Genomics51
    • Personal genomics51
    • Synthetic genomics51
    • Viral genomics51
    • Whole genomes51
    • Comparative transcriptomics50
    • Transcriptome50
    • Transcriptomics50
    • Biostatistics44
    • Descriptive statistics44
    • Gaussian processes44
    • Inferential statistics44
    • Markov processes44
    • Multivariate statistics44
    • Probabilistic graphical model44
    • Probability44
    • Statistics44
    • Statistics and probability44
    • Biodiversity41
    • Bottom-up proteomics38
    • Discovery proteomics38
    • MS-based targeted proteomics38
    • MS-based untargeted proteomics38
    • Metagenomics38
    • Metaproteomics38
    • Peptide identification38
    • Protein and peptide identification38
    • Proteomics38
    • Quantitative proteomics38
    • Shotgun metagenomics38
    • Targeted proteomics38
    • Top-down proteomics38
    • Biological sequences35
    • Sequence analysis35
    • Sequence databases35
    • Assembly34
    • Sequence assembly34
    • Community analysis33
    • Environmental microbiology33
    • Microbial ecology33
    • Microbiome33
    • Molecular community analysis33
    • Computational ecology30
    • Ecoinformatics30
    • Ecological informatics30
    • Ecology30
    • Ecosystem science30
    • Diffraction experiment24
    • Imaging24
    • Microscopy24
    • Microscopy imaging24
    • Optical super resolution microscopy24
    • Photonic force microscopy24
    • Photonic microscopy24
    • Antimicrobial stewardship22
    • Medical microbiology22
    • Microbial genetics22
    • Microbial physiology22
    • Microbial surveillance22
    • Microbiological surveillance22
    • Microbiology22
    • Molecular infection biology22
    • Molecular microbiology22
    • DNA variation19
    • Genetic variation19
    • Genomic variation19
    • Mutation19
    • Polymorphism19
    • Somatic mutations19
    • Taxonomy17
    • Epigenomics14
    • Data management13
    • Metadata management13
    • Open science13
    • Research data management (RDM)13
    • Exometabolomics12
    • FAIR data12
    • Findable, accessible, interoperable, reusable data12
    • LC-MS-based metabolomics12
    • MS-based metabolomics12
    • MS-based targeted metabolomics12
    • MS-based untargeted metabolomics12
    • Mass spectrometry-based metabolomics12
    • Metabolites12
    • Metabolome12
    • Metabolomics12
    • Metabonomics12
    • NMR-based metabolomics12
    • Evolution10
    • Evolutionary biology10
    • Show N_FILTERS more
    • Tool
    • Galaxy34
    • scikit-learn16
    • GEMINI1
    • IWTomics1
    • PubMed1
    • Show N_FILTERS more
    • Content provider
    • GTN44
    • Show N_FILTERS more
    • Keyword
    • Statistics and machine learning43
    • ai-ml9
    • elixir9
    • jupyter-notebook8
    • Large Language Model5
    • interactive-tools4
    • work-in-progress3
    • deep-learning2
    • jupyter-lab2
    • machine-learning2
    • Deep Learning1
    • Digital Humanities1
    • GLEAM1
    • HAM10000 Dataset1
    • HANCOCK Dataset1
    • Image Classification1
    • Image Learner1
    • LORIS Score Model1
    • Machine Learning1
    • Machine learning1
    • Multimodal Learning1
    • Pan-cancer1
    • Recurrence Prediction1
    • Skin Lesion Classification1
    • Tabular Learner1
    • cancer biomarkers1
    • dephosphorylation-site-prediction1
    • fine-tuning1
    • image-segmentation1
    • oncogenes and tumor suppressor genes1
    • protein-3D-structure1
    • text mining1
    • Show N_FILTERS more
    • Competency level
    • Beginner29
    • Intermediate15
    • Show N_FILTERS more
    • Licence
    • Creative Commons Attribution 4.0 International44
    • Show N_FILTERS more
    • Target audience
    • Students
    • PhD students3
    • Clinicians2
    • biologists2
    • Analysts1
    • Beginners in statistics1
    • Life Science Researchers1
    • Postgraduate students1
    • Research Software Engineers1
    • Researchers1
    • Trainers1
    • Training Designers1
    • Training instructors1
    • statisticians1
    • Show N_FILTERS more
    • Author
    • Anup Kumar11
    • Kaivan Kamali8
    • Bérénice Batut6
    • Jeremy Goecks6
    • Junhao Qiu6
    • Paulo Cilas Morais Lyra Junior6
    • Raphael Mourad5
    • Alireza Khanteymoori4
    • Amirhossein Naghsh Nilchi4
    • Björn Grüning4
    • Khai Van Dang4
    • Alyssa Pybus2
    • Daniel Blankenberg2
    • Fabio Cumbo2
    • Fotis E. Psomopoulos2
    • Ralf Gabriels2
    • Simon Bray2
    • Daniela Schneider1
    • Dennis Lal group1
    • Ekaterina Polkh1
    • Marie Gramm1
    • Marzia A Cremona1
    • Polina Polunina1
    • Stella Fragkouli1
    • Vijay1
    • Wandrille Duchemin1
    • Show N_FILTERS more
    • Contributor
    • Saskia Hiltemann31
    • Björn Grüning28
    • Anup Kumar27
    • Helena Rasche19
    • Martin Čech15
    • Bérénice Batut14
    • Armin Dadras11
    • Teresa Müller9
    • Kaivan Kamali8
    • Paulo Cilas Morais Lyra Junior6
    • Alireza Khanteymoori5
    • Fabio Cumbo5
    • Wandrille Duchemin5
    • olisand5
    • Amirhossein Naghsh Nilchi4
    • Cristóbal Gallardo2
    • Delphine Lariviere2
    • Gildas Le Corguillé2
    • Junhao Qiu2
    • Michelle Terese Savage2
    • Nate Coraor2
    • Simon Bray2
    • qiagu2
    • Anthony Bretaudeau1
    • Bert Droesbeke1
    • Daniel Blankenberg1
    • Daniel Sobral1
    • Daniela Schneider1
    • Enis Afgan1
    • Mohammad Joudy1
    • Mélanie Petera1
    • Niall Beard1
    • Nicola Soranzo1
    • Pavankumar Videm1
    • Stella Fragkouli1
    • Vijay1
    • dlal-group1
    • Show N_FILTERS more
    • Resource type
    • e-learning35
    • slides9
    • Show N_FILTERS more
    • Related resource
    • Associated Training Datasets34
    • Associated Workflows31
    • Jupyter Notebook (with Solutions)9
    • Jupyter Notebook (without Solutions)9
    • Show N_FILTERS more
  • Show materials from all spaces
  • Show disabled materials
  • Show materials with broken links
  • Show archived materials

Training materials

  • Subscribe via email

Email Subscription

Register training material

Scientific topics: Bayesian methods

and Target audience: Students

44 materials found
  • slides

    Image classification in Galaxy with fruit 360 dataset

    • Beginner
    Statistics and probability Statistics and machine learning
  • slides

    Convolutional neural networks (CNN) Deep Learning - Part 3

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

    Introduction to deep learning

    • Beginner
    Statistics and probability Statistics and machine learning
  • slides

    Feedforward neural networks (FNN) Deep Learning - Part 1

    • Beginner
    Statistics and probability Statistics and machine learning
  • 1
  • 2
  • 3
  • 4
  • 5
Training eSupport System
[email protected]
Contribute
About TeSS
Browse Spaces
Funding & acknowledgements
Privacy
Cookie preferences
Version: 1.5.1
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.