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rfuentealba

September 26

¿Cuántos y quiénes somos? Quedan cordialmente invitados a presentarse.

¡Hola, Comunidad! Primero que todo, gusto de saludarles, gracias por el apoyo. Si bien yo puse la primera piedra, ahora es el turno de ustedes, porque la comunidad de usuarios la hacemos todos. Quiero iniciar este hilo para que nos conozcamos un poco más, y podamos comenzar a generar un poco de contenido. Mi nombre es Rodrigo, soy de Las Condes, una comuna de la Región Metropolitana de Santiago, la capital de Chile. Tengo 35 años y llevo cerca de 20 años trabajando con bases de datos (así es, desde que estaba en el colegio), especialmente con PostgreSQL. Hace 10 años que he implementado "algunas cositas estadísticas" usando el lenguaje de programación Ruby, y sólo hace poco me enteré de que muchas de esas cositas estadísticas son, en verdad, algoritmos bastante rudimentarios de ciencia de datos (conocida es la historia que cuento de que el algoritmo "Bayes Inocente" debe su nombre a Sir Thomas Bayes que lo creó en el siglo XVIII, y a mí, el Inocente que creyó que lo había creado en... 2008.), así es que poder adaptarme a lo que RapidMiner entrega no me fue tan difícil. Además, cuento con algo de conocimiento sobre estadística y economía gracias a un intercambio que hice en 2002, mientras estudiaba en la U. Este año me convertí en RapidMiner Certified Analyst y Partner Ambassador de RapidMiner, y además la empresa para la que trabajo es un VAR partner de RapidMiner, así es que si tienen dudas con el licenciamiento o cosas así, puedo responder acerca de eso también, siempre con toda la ayuda de @sgenzer y de @leti. Pues nada, bienvenidos todos a la comunidad. Un abrazo enorme desde Chile,

jacobcybulski

September 15

RM Server 9 running great on Paperspace with GPUs

I have noticed that Paperspace is not featuring strongly in the RM community (in favour of AWS and Azure), however, if you needed to play with deep learning with large GPUs and little expense (e.g. Volta V100 is $2.30 per hour), I can report that the RM Server 9 installs and runs just great on their Ubuntu virtual machines (I have not tested their Windows machines).   Things to watch: make sure you install Java 8 (as Java 9 is a default), install Oracle MySQL (use legacy passwords and the current Oracle mysql connector) and avoid Mariadb (unless you want a major cleanup of your machine) and define your Keras and Python settings in [/job-agent/home/config/rapidminer/rapidminer.properties], pointing to the Python in the preinstalled Anaconda with Tensorflow and Keras.   As Keras plugin seems memory hungry, increase the container memoryLimit to the max you can (depending on your machine configuration) in [/job-agent/home/config/agent.properties].   Last, you can save yourself a lot of time and effort by steering away from bare machines, and instead using some of the pre-configured ML in a Box templates, which they currently have for small GPUs (which I was playing with) and for Volta V100 (in Beta).   Have fun -- Jacob P.S. I found that a container memory limit less than 8Gb causes frequent over the limit warnings, however, Java seemed to recover and garbagge collect the memory to complete a job.

pschlunder

August 14

A more native Deep Learning solution

Let me introduce you our new Deep Learning extension! At its core, this new extension focuses on bringing you a more RapidMiner-native Deep Learning experience. The initial release gets you started with ExamplesSets and explores textual data, running on CPU or GPU as you like.   With this new extension the entry hurdle of creating your first Deep Learning model is lower than before; there is no need of installing anything but the extension (download it here)! There is no need to set up input shapes! And there is no need for an extra operator to apply your model on ExampleSets! There are more VISUALS, more GUIDANCE, and more SPEED due to a java-based execution! More visual layer architecture, thanks to explanatory and individual icons per layer. Sample processes include guidance on different architectures, how to handle various data types, how to optimize networks, and learning how to use this operator: The new Deep Learning extension provides an updateable model. This feature allows to continue training further on. So if the training took a while and new data is present, just continue training with it instead of having to start all over again. A growing number of tutorial processes, including tips and tricks. To speed up things with big data sets, execution on GPU is also available. You'll find the switch in the general settings: Deep Learning techniques require numerical data, so conversion methods for text are needed. A first operator for text-to-numerical conversion is now included: The extension is powered by the open-source library DeepLearning4J (version 1.0.0-beta). Check out their website and github.   We're just getting started with this native integration, so stay tuned for more features and types of data being easily usable for Deep Learning in RapidMiner! Philipp  

Knut-RM

May 15

Importing Data in RapidMiner Studio

  Download the data here!   * Importing data and storing it within RapidMiner * Operators, Parameters and Connectors * Retrieve operator * ‘Attributes’ are Columns of the data and ‘Example Set’ are Rows of Data * Adjusting the layout * Creating new Repositories with sub-folders ‘Data’ & ‘Processes’ * Accessing Help & utilizing Tutorial Process

Knut-RM

May 15

Visualizing Data in RapidMiner Studio

  * Sections under Results: Data, Statistics, Charts, Advanced Charts & Annotations * Understanding terminology in Data tab: Example set – Entire data, Examples – Rows, Attributes – Columns * Rearrange – Change size/width, Sequence & Sort order of columns * Understanding Statistics section to review data * Types of Visualizations/Charts

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  47 views 3 comments 0 points Most recent by varunm1 November 15Knowledge Base
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  Responded But No Solution 20 views 1 comment 0 points Most recent by mschmitz November 14Help
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  Responded But No Solution 20 views 2 comments 0 points Most recent by sgenzer November 14Help
• Keras in Rapid Miner

  Responded But No Solution 15 views 1 comment 0 points Most recent by sgenzer November 14Help
• how to implement python code for the text mining process ?

  Responded But No Solution 29 views 1 comment 0 points Most recent by sgenzer November 14Help