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GMU:My Computer, Max, and I/Pedro Ramos (view source)
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, 12 December 2019→Machine Learning
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The interest of working with Machine Learning integrated to Max/MSP consisted on different aspects and practicalities, such as the own limited availability of resources on- and offline of such integration, limited knowledge on other training methods on other environments usually used for Machine Learning (such as Python and C++), and altogether the interest in the creative possibilities of working with such regardless of technical knowledge limitation on other environments. The integration of such possibilities within Max/MSP aimed, as a consequence, to expand the own creative and technical possibilities that it might offer as a tool integrated within Max/MSP. | The interest of working with Machine Learning integrated to Max/MSP consisted on different aspects and practicalities, such as the own limited availability of resources on- and offline of such integration, limited knowledge on other training methods on other environments usually used for Machine Learning (such as Python and C++), and altogether the interest in the creative possibilities of working with such regardless of technical knowledge limitation on other environments. The integration of such possibilities within Max/MSP aimed, as a consequence, to expand the own creative and technical possibilities that it might offer as a tool integrated within Max/MSP. | ||
For such, a third party software called Wekinator was used as main element for integration and training. It was developed by Dr. Rebecca Fiebrink, a senior lecturer in Computing at Goldsmiths University of London and member of the Embodied Audiovisual Interaction (EAVI) group of the university. The software, which first version was developed in 2012, aim is to democratise the possibilities of working with Machine Learning for creators, making it available for being used without knowledge in some of the other programming environments, which might be fairly more complex than Wekinator, which can be integrated to a varied array of Softwares already familiar to each creator. In this case, Max/MSP, but it could have been integrated to many other softwares that facilitate external integration - such as Processing, Supercollider, amongst others - as | For such, a third party software called Wekinator was used as main element for integration and training. It was developed by Dr. Rebecca Fiebrink, a senior lecturer in Computing at Goldsmiths University of London and member of the Embodied Audiovisual Interaction (EAVI) group of the university. The software, which first version was developed in 2012, aim is to democratise the possibilities of working with Machine Learning for creators, making it available for being used without knowledge in some of the other programming environments, which might be fairly more complex than Wekinator, which can be integrated to a varied array of Softwares already familiar to each creator. In this case, Max/MSP, but it could have been integrated to many other softwares that facilitate external integration - such as Processing, Supercollider, amongst others - as its connection for training takes place through Open Sound Control (OSC). | ||
In the training of Machine Learning Algorithms, two different training possibilities are possible: Supervised and Unsupervised Learning. In short, Supervised Learning consists on algorithmically training that specifies an input and a correspondent output regarding a specific Data. That means that it’s possible to somehow determine what’s the supposed Data Output from a dataset training. On Unsupervised Learning, the training considers a determined Data Input, but no specific Output is determined. That means that the resulted output can consists on unexpected combined data sets. Each Training system can have better appliances for different environments and uses of Machine Learning for data processing. | In the training of Machine Learning Algorithms, two different training possibilities are possible: Supervised and Unsupervised Learning. In short, Supervised Learning consists on algorithmically training that specifies an input and a correspondent output regarding a specific Data. That means that it’s possible to somehow determine what’s the supposed Data Output from a dataset training. On Unsupervised Learning, the training considers a determined Data Input, but no specific Output is determined. That means that the resulted output can consists on unexpected combined data sets. Each Training system can have better appliances for different environments and uses of Machine Learning for data processing. | ||