Q11easyIn the context of weights, what is Weight Initialization?ASetting all weights to 1.BSetting all weights to 0.CStarting weights with small, random numbers to ensure different neurons learn different features.DLoading weights from a previous project.Check AnswerReveal Answer & ExplanationDiscuss on Board
Q12hardWhat is the main purpose of Batch Normalization?ATo increase the number of layers.BTo compress the weights to 0.CTo stabilize the learning process by normalizing inputs to each layer, allowing for higher learning rates.DTo reduce the number of input features.Check AnswerReveal Answer & ExplanationDiscuss on Board
Q13easyWhat is a Hidden Layer?AThe layer that outputs the final answer.BA layer between the input and output layers where feature extraction occurs.CA layer that only contains bias.DA layer that is only used for testing.Check AnswerReveal Answer & ExplanationDiscuss on Board
Q14mediumA developer chooses to use a ReLU activation function instead of Sigmoid. What is the most significant benefit?AReLU works better for binary output.BReLU avoids the "vanishing gradient" problem for positive values, allowing for faster convergence.CReLU is computationally more expensive.DReLU produces values between 0 and 1.Check AnswerReveal Answer & ExplanationDiscuss on Board
Q15mediumWhich function is typically used in the output layer of a Multi-class Classification problem?ASoftmaxBTanhCReLUDSigmoidCheck AnswerReveal Answer & ExplanationDiscuss on Board
Q16easyWhat does the Loss Function measure in deep learning training?AThe number of training epochs completed.BThe speed of the GPU.CThe disparity between the predicted output and the actual target label.DHow much memory the model uses.Check AnswerReveal Answer & ExplanationDiscuss on Board
Q17easyWhat does the Optimizer do?AIt defines the neural network architecture.BIt converts the data to JSON.CIt implements the Gradient Descent logic to update the weights based on the loss function.DIt chooses the dataset.Check AnswerReveal Answer & ExplanationDiscuss on Board
Q18hardIn a simple neuron, the output is calculated as Output = Activation(Sum(Input * Weight) + Bias). What is the role of the Bias term?ATo force the output to be negative.BTo normalize the learning rate.CTo shift the activation function, allowing the model to fit data that doesn't pass through the origin (0,0).DTo multiply the inputs.Check AnswerReveal Answer & ExplanationDiscuss on Board