Question: An MLP is used to classify the training points into blue and orange classes. Input features ( x 1 , x 2 ) are transformed

An MLP is used to classify the training points into blue and orange classes. Input features (x1,x2) are transformed into y=sqrt(x1^2+X2^2)
. The MLP accepts y as the input data. What is the minimum number of hidden layers needed to classify the data accurately?What are the different features of Big Data Analytics?

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