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I could be misunderstanding the question but I think it's worth distinguishing between whether we are regressing or optimizing. From your equation is looks like $V_t$ is an input variable which you are using to make predictions. However from your phrasing of the question it sounds more like $V_t$ is itself an function of price and you are looking to maximise ...


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I had the same problem, restudy the equations, and found that in my coding, I mixed up the n, size of the whole training set, with the m, size of the mini batch. Based on my understanding, for the weight decay term calculation, n, size of the whole training set should be used. Whereas, m, size of the mini batch is used in the approximation of gradient of ...


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The cv.glmnet function uses k-fold cross-validation to estimate an optimal penalty term. The default for this software is to use 10 folds. So, the software fits many ridge regressions on a grid of different penalty values and then chooses the value of the penalty parameter that minimizes estimated out-of-sample prediction error, using cross-validation to ...


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Weights determine slopes of the activation functions. Regularization reduces the weights and hence the slopes of the activation functions. This reduces the model variance and the overfitting effect. The biases have no influence on the slopes of activation functions. However, they have an influence on the position of the activation functions in space. Their ...


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