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Introduction to Deep Learning and Generative AI, Quiz 1
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Consider the following data point and weight vector in a simple single layer neural network. (Assume that there is no bias.) x = [1,0] w = [0,1] Once the data flows through the simple network and is passed through the activation function, the final output comes out to be 0.5. Once you have identified the activation function, enter the maximum value that the derivative of the activation function can take.
Consider the following data point and weight vector in a simple single layer neural network. (Assume that there is no bias.) x = [1,0] w = [0,1] Once the data flows through the simple network and is passed through the activation function, the final output comes out to be 0.5. Once you have identified the activation function, enter the maximum value that the derivative of the activation function can take. A BatchNorm layer is applied to the output of a convolutional layer. The output feature map has dimensions: where: 32 = Batch size (number of images in the mini-batch)• 28 = Height of each feature map• 28 = Width of each feature map• 64 = Number of channels• How many learnable parameters does this BatchNorm layer contain? Consider a McCulloch-Pitts (MP) neuron with binary inputs, unit positive weights, and no inhibitory connections. Given the following data points: a = [0,0] b = [1,1] c = [1,0] The neuron must satisfy: a → 0 b → 0 c → 1 Which of the following statements is TRUE (θ is the threshold for the neuron to fire.)?