Difference between revisions of "Graphical Tools for Modeling AI Components"
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These range from a new interface for a tool that completely automates the process of creating models, to a new no-code visual interface for building, training and deploying models, all the way to hosted Jupyter-style notebooks for advanced users. Tools that allow visual drag-and-drop interface to streamline and simplify your process | These range from a new interface for a tool that completely automates the process of creating models, to a new no-code visual interface for building, training and deploying models, all the way to hosted Jupyter-style notebooks for advanced users. Tools that allow visual drag-and-drop interface to streamline and simplify your process | ||
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| − | + | * [http://www.knime.com/ Knime] | |
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* [http://perceptilabs.readme.io/docs PerceptiLabs] | * [http://perceptilabs.readme.io/docs PerceptiLabs] | ||
* [http://lobe.ai/ lobe] | * [http://lobe.ai/ lobe] | ||
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* Orange | * Orange | ||
* Dataiku | * Dataiku | ||
| − | * Dianne | + | * [http://dianne.intec.ugent.be/ Dianne] |
| + | * [[TensorFlow]]... | ||
| + | ** [http://playground.tensorflow.org Playground] | ||
| + | ** [[TensorBoard]] | ||
| + | * [http://developer.nvidia.com/digits NVIDIA Deep Learning GPU Training System (DIGITS)] | ||
* [[H2O]] - [http://www.h2o.ai/products/h2o-driverless-ai/ Driverless AI] | * [[H2O]] - [http://www.h2o.ai/products/h2o-driverless-ai/ Driverless AI] | ||
* [[Microsoft]] [http://azure.microsoft.com/en-us/services/machine-learning/ Azure] - [http://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-create-portal-experiments Azure Machine Learning studio] | * [[Microsoft]] [http://azure.microsoft.com/en-us/services/machine-learning/ Azure] - [http://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-create-portal-experiments Azure Machine Learning studio] | ||
Revision as of 14:02, 7 December 2019
YouTube Search ...Google search
- Development
- Model Zoo
- 10 Tools for Modeling AI Components – Machine Learning without the code | Jordi Cabot - Modeling Languages
These range from a new interface for a tool that completely automates the process of creating models, to a new no-code visual interface for building, training and deploying models, all the way to hosted Jupyter-style notebooks for advanced users. Tools that allow visual drag-and-drop interface to streamline and simplify your process
- Knime
- PerceptiLabs
- lobe
- CognitiveScale - Cortex Studio
- RapidMiner
- Orange
- Dataiku
- Dianne
- TensorFlow...
- NVIDIA Deep Learning GPU Training System (DIGITS)
- H2O - Driverless AI
- Microsoft Azure - Azure Machine Learning studio
- IBM - Watson Studio SPSS Modeler
