Materials - TeSS (Training eSupport System)

Log In

About
Events
Materials
e-Learning
Workflows
Collections
Directory
- Providers
- Nodes

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

Home
Materials

Filters

Sort

Filter Clear filters
- Scientific topic
- R14
- R program14
- R script14
- Active learning2
- Ensembl learning2
- Kernel methods2
- Knowledge representation2
- Machine learning2
- Neural networks2
- Recommender system2
- Reinforcement learning2
- Supervised learning2
- Unsupervised learning2
- Show N_FILTERS more
- Content provider
- Glittr.org17
- Show N_FILTERS more
- Keyword
- R15
- Machine learning2
- Python2
- Quarto2
- Shiny2
- Containerization1
- Docker1
- Version control1
- Show N_FILTERS more
- Difficulty level
- Not specified17
- Show N_FILTERS more
- Author
- posit::conf(2024)17
- Show N_FILTERS more
- Node
- Switzerland17
- Show N_FILTERS more
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

Frequency

Register training material

Contributors: posit::conf(2024)

17 materials found

posit-conf-2024/quarto-websites

R script Quarto R
posit-conf-2024/shiny-r-intro

R script R Shiny
posit-conf-2024/ds-workflows-r

R script R
posit-conf-2024/databases

R script R
posit-conf-2024/level-up-shiny

R script R Shiny
posit-conf-2024/pkg-dev

R script R
posit-conf-2024/r-in-production

R script R

1
2

[email protected]

Contribute

About TeSS

Funding & acknowledgements

Privacy

Cookie preferences

Version: 1.4.1

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.