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 methods43
    • Biostatistics43
    • Descriptive statistics43
    • Gaussian processes43
    • Inferential statistics43
    • Markov processes43
    • Multivariate statistics43
    • Probabilistic graphical model43
    • Probability43
    • Statistics43
    • Statistics and probability43
    • Show N_FILTERS more
    • Tool
    • Galaxy33
    • scikit-learn16
    • GEMINI1
    • IWTomics1
    • PubMed1
    • Show N_FILTERS more
    • Content provider
    • GTN43
    • Show N_FILTERS more
    • Keyword
    • Statistics and machine learning
    • R319
    • Transcriptomics152
    • Genomics148
    • Python142
    • Next generation sequencing115
    • RNA-seq115
    • Data management101
    • Bioinformatics94
    • Statistics77
    • FAIR data76
    • Galaxy Server administration68
    • Machine learning67
    • FAIR66
    • Reproducibility65
    • Data science64
    • Unix/Linux61
    • Single-cell sequencing59
    • data management54
    • jupyter-notebook54
    • Proteomics53
    • Workflows52
    • Foundations of Data Science50
    • Data visualization49
    • Version control49
    • Variant analysis48
    • biodiversity47
    • Development in Galaxy45
    • Genome assembly43
    • Single Cell41
    • microgalaxy41
    • training38
    • General36
    • Genome annotation36
    • Metagenomics36
    • FAIR Data, Workflows, and Research33
    • Microbiome32
    • Genome Annotation31
    • Nextflow31
    • Ecology30
    • programming30
    • Genome28
    • Molecular28
    • Assembly27
    • Contributing to the Galaxy Training Material27
    • Metadata27
    • Using Galaxy and Managing your Data27
    • metagenomics27
    • Data analysis26
    • Epigenetics26
    • Long read sequencing26
    • Phylogenetics25
    • RDM25
    • Data reuse24
    • FAIR principles24
    • Containerization23
    • High performance computing23
    • Docker22
    • Microbiology22
    • Spatial transcriptomics22
    • data stewardship22
    • Open Science21
    • Rare Diseases & Research21
    • Shiny21
    • ansible21
    • reproducible research21
    • Imaging20
    • metadata20
    • sensitive data19
    • CfRR19
    • ChIP-seq19
    • FAIR Data19
    • Galaxy19
    • Metabolomics19
    • REDCap19
    • git-gat19
    • life-sciences19
    • scientific computing19
    • Cloud computing18
    • Comparative genomics18
    • Programming18
    • Variant Analysis18
    • interactive-tools18
    • Artificial intelligence17
    • Climate17
    • Data sharing17
    • Sequence analysis17
    • Teaching and Hosting Galaxy training17
    • work-in-progress17
    • HemaFAIR16
    • Introduction to Galaxy Analyses16
    • Research Data Management16
    • Roslin Institute16
    • Shell16
    • fair16
    • jbrowse116
    • Data mining15
    • Quarto15
    • bioinformatics15
    • earth-system15
    • Show N_FILTERS more
    • Competency level
    • Beginner29
    • Intermediate14
    • Show N_FILTERS more
    • Licence
    • Creative Commons Attribution 4.0 International43
    • Show N_FILTERS more
    • Target audience
    • Students43
    • 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
    • Dennis Lal group1
    • Ekaterina Polkh1
    • Marie Gramm1
    • Marzia A Cremona1
    • Polina Polunina1
    • Stella Fragkouli1
    • Vijay1
    • Wandrille Duchemin1
    • Show N_FILTERS more
    • Contributor
    • Saskia Hiltemann30
    • Anup Kumar27
    • Björn Grüning27
    • Helena Rasche18
    • Martin Čech15
    • Bérénice Batut14
    • Armin Dadras11
    • Kaivan Kamali8
    • Teresa Müller8
    • 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
    • Enis Afgan1
    • Mohammad Joudy1
    • Mélanie Petera1
    • Niall Beard1
    • Nicola Soranzo1
    • Pavankumar Videm1
    • Stella Fragkouli1
    • Vijay1
    • dlal-group1
    • Show N_FILTERS more
    • Resource type
    • e-learning34
    • slides9
    • Show N_FILTERS more
    • Related resource
    • Associated Training Datasets33
    • Associated Workflows30
    • 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
  • Hide archived materials

Training materials

  • Subscribe via email

Email Subscription

Register training material

Keywords: Statistics and machine learning

and Include archived: true

43 materials found
  • 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.