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Open-Ended Respоnse. Undergrаduаte students оnly: Explаin the rоles of convolution, activation, pooling, flattening, dense layers, logits, and softmax in an image-classification convolutional neural network (CNN). Clearly distinguish a logit from a class probability and explain how the predicted class is selected. Expecting a few short sentences or a longer paragraph.Graduate students only: Two regression models estimate battery state of charge. Model A has lower mean squared error (MSE), but Model B has lower mean absolute error (MAE). Explain how different residual distributions could produce this result. Discuss what MSE, root mean squared error (RMSE), MAE, and the coefficient of determination R2 reveal or conceal about performance. Recommend a model when occasional large errors are especially costly and justify your choice. Expecting a few well-thought paragraphs. Do not give bulleted lists.
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