3. Suppose a simple single layer perceptron with 2-d input X= (x1, x2), a bias., ReLU for activation function, and MSE (mean squared error) for loss function. D D D®) D4) X1 -1 1 X2 -1 Show evolution...

3.<br>Suppose a simple single layer perceptron with 2-d input X= (x1, x2), a bias., ReLU for activation<br>function, and MSE (mean squared error) for loss function.<br>D D<br>D®)<br>D4)<br>X1<br>-1<br>1<br>X2<br>-1<br>Show evolution of weights according to stochastic gradients algorithm with batch size =1<br>Show evolution of weights according to stochastic gradients algorithm with batch sizel=2<br>a.<br>b.<br>

Extracted text: 3. Suppose a simple single layer perceptron with 2-d input X= (x1, x2), a bias., ReLU for activation function, and MSE (mean squared error) for loss function. D D D®) D4) X1 -1 1 X2 -1 Show evolution of weights according to stochastic gradients algorithm with batch size =1 Show evolution of weights according to stochastic gradients algorithm with batch sizel=2 a. b.

Jun 11, 2022

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3. Suppose a simple single layer perceptron with 2-d input X= (x1, x2), a bias., ReLU for activation function, and MSE (mean squared error) for loss function. D D D®) D4) X1 -1 1 X2 -1 Show evolution...

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