Machine Learning Engineer

San Francisco

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Questions for Applicants - Machine Learning Engineer (required)

  • To design a neural network based predictor for f(x) = sin(x) we designed the following network. Input ----> Dense(num_neurons=1) ---> Relu() ----> Dense(num_neurons=100) ---> Relu() ---> Dense(num_neurons=1) ----> Relu()--> Output Our choice of loss was L2 and optimizer was standard gradient descent. The predictor was trained on X_train = numpy.arange(0.0, 314.1, 0.1) and Y_train = numpy.sin(X_train). It is subsequently tested on X_test= numpy.arange(-10.0, 10.0, 0.001) and Y_test = numpy.sin(X_test) The predictor however performs badly on the test data. What could have gone wrong?

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