Difference between revisions of "Graphical Tools for Modeling AI Components"
| Line 19: | Line 19: | ||
** [http://playground.tensorflow.org Playground] | ** [http://playground.tensorflow.org Playground] | ||
** [[TensorBoard]] | ** [[TensorBoard]] | ||
| − | * | + | * http://developer.nvidia.com/digits NVIDIA Deep Learning GPU Training System (DIGITS)] |
* Knime | * Knime | ||
* [http://perceptilabs.readme.io/docs PerceptiLabs] | * [http://perceptilabs.readme.io/docs PerceptiLabs] | ||
Revision as of 13:58, 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
- TensorFlow...
- http://developer.nvidia.com/digits NVIDIA Deep Learning GPU Training System (DIGITS)]
- Knime
- PerceptiLabs
- lobe
- CognitiveScale - Cortex Studio
- RapidMiner
- Orange
- Dataiku
- Dianne
- H2O - Driverless AI
- Microsoft Azure - Azure Machine Learning studio
- IBM - Watson Studio SPSS Modeler
