{"repo":"nf-core/deepmodeloptim","free":true,"listed":false,"github":"https://github.com/nf-core/deepmodeloptim","clone":"git clone https://github.com/nf-core/deepmodeloptim.git","description":"Stochastic Testing and Input Manipulation for Unbiased Learning Systems","language":"Nextflow","stars":31,"topics":["artificial-intelligence","dataprocessing","deeplearning","genomics","large-scale","optimization","prototyping-tool","nextflow","nf-core","pipeline"],"license":"MIT","category":"machine-learning","readme_excerpt":"📌 Quick intro check out this 👉🏻 video! Introduction nf-core/deepmodeloptim augments your bio data towards an optimal task-specific training set. Methods in deep learning are vastly equivalent (see neural scaling laws paper), most of the performance is driven by the training data. Usage [!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data. Now, you can run the pipeline using: [!WARNING] Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters ; see docs. For more details and further functionality, please refer to the usage documentation and the parameter documentation. Pipeline output To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation. Code requirements Data The data is provided as a csv where the header columns are in the following format : name:type:class name is user given (note that it has an impact on experiment definition). type is either \"input\", \"meta\", or \"label\". \"input\" types are fed into the mode, \"meta\" types are registered but not transformed nor fed into the models and \"label\" is used as a training ","default_branch":null,"files":null,"tree":[],"storefront":"/r/nf-core","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/nf-core/deepmodeloptim/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}