Many people in Singapore like to eat durian. Many customers believe that a perfectly oval and...
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Many people in Singapore like to eat durian. Many customers believe that a perfectly oval and rounded durian is not always the best. An odd-shaped fruit that comes in slightly curved and crescent shape may taste better. You decide to train an image classifier to predict whether a durian is with rounded shape (label=0) or odd shape (label=1). i) You've collected your own labeled dataset, chosen a neural network architecture, and are thinking about using the mean squared error (MSE) loss to optimize model parameters. Give one reason why MSE might not be a good choice for your loss function. (1 mark) ii) You decide to use the binary cross-entropy (BCE) loss to optimize your network. Write down the formula for this loss (for a single example) in terms of the label y and prediction ŷ. (1 mark) iii) Compute the total cost, J, of the network averaged across the following dataset of three examples using the binary cross entropy loss. Y = (1,0,0), and Ŷ = (0.2, 0.5, 0.1)T. There is no penalty on the weights. (2 mark) iv) You decide to train one model with L2 regularization (model A) and one without (model B). How would you expect model A's weights to compare to model B's weights? Activate (1 mark)ett Many people in Singapore like to eat durian. Many customers believe that a perfectly oval and rounded durian is not always the best. An odd-shaped fruit that comes in slightly curved and crescent shape may taste better. You decide to train an image classifier to predict whether a durian is with rounded shape (label=0) or odd shape (label=1). i) You've collected your own labeled dataset, chosen a neural network architecture, and are thinking about using the mean squared error (MSE) loss to optimize model parameters. Give one reason why MSE might not be a good choice for your loss function. (1 mark) ii) You decide to use the binary cross-entropy (BCE) loss to optimize your network. Write down the formula for this loss (for a single example) in terms of the label y and prediction ŷ. (1 mark) iii) Compute the total cost, J, of the network averaged across the following dataset of three examples using the binary cross entropy loss. Y = (1,0,0), and Ŷ = (0.2, 0.5, 0.1)T. There is no penalty on the weights. (2 mark) iv) You decide to train one model with L2 regularization (model A) and one without (model B). How would you expect model A's weights to compare to model B's weights? Activate (1 mark)ett
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Forensic Accounting and Fraud Examination
ISBN: 978-0078136665
2nd edition
Authors: William Hopwood, george young, Jay Leiner
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