GMU:My Computer, Max, and I/Pedro Ramos: Difference between revisions

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The brief explanation on some of the aspects just explained, for the work of Wekinator, in which the training is itself limited regarding the specifications on some of such aspects, such as weights, number of layers on Neural Network training, amongst others, can however be important. Although the software works as a black-box, that means, it’s not really possible to grasp everything that is happening on an algorithmically level, and for such gives the creator less autonomy on the training process and possible outputs, it although already offers the possibility of working with different types of Training Methods, such as Neural Networks and Linear or Polynomial Regression. For such, and in order to achieve bigger autonomy on the training processes despite such limitations, the comprehension of some key terms on Machine Learning can make itself necessary or at least helpful, namely: classifiers, Backpropagation, decision stumps, amongst others.

===~~Overview on~~ Training with Wekinator===

===Training with Wekinator===

Considering the short incursion on generals on Machine Learning, and the decision on working with Wekinator considering the opportunities it offers, and its limitations, a short description on the connection between Max/MSP and Wekinator might be made necessary. Considering the last developed patch for the system, follows a brief step-by-step on the Inputs, Model Processing and training, and Output back to Max/MSP.

@@ Line 31: / Line 31: @@
 The brief explanation on some of the aspects just explained, for the work of Wekinator, in which the training is itself limited regarding the specifications on some of such aspects, such as weights, number of layers on Neural Network training, amongst others, can however be important. Although the software works as a black-box, that means, it’s not really possible to grasp everything that is happening on an algorithmically level, and for such gives the creator less autonomy on the training process and possible outputs, it although already offers the possibility of working with different types of Training Methods, such as Neural Networks and Linear or Polynomial Regression. For such, and in order to achieve bigger autonomy on the training processes despite such limitations, the comprehension of some key terms on Machine Learning can make itself necessary or at least helpful, namely: classifiers, Backpropagation, decision stumps, amongst others.
-===Overview on Training with Wekinator===
+===Training with Wekinator===
 Considering the short incursion on generals on Machine Learning, and the decision on working with Wekinator considering the opportunities it offers, and its limitations, a short description on the connection between Max/MSP and Wekinator might be made necessary. Considering the last developed patch for the system, follows a brief step-by-step on the Inputs, Model Processing and training, and Output back to Max/MSP.