APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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a way to determine how well the machine learning model has performed given the different values of each parameter
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method to increase the speed of Neural Network operation
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an optimization algorithm used to find the values of parameters (coefficients) of a function (f) that minimizes a cost function (cost)
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different name for activation function
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Detailed explanation-1: -Gradient descent is an optimization algorithm which is commonly-used to train machine learning models and neural networks. Training data helps these models learn over time, and the cost function within gradient descent specifically acts as a barometer, gauging its accuracy with each iteration of parameter updates.
Detailed explanation-2: -Gradient Descent is an optimization algorithm for finding a local minimum of a differentiable function. Gradient descent in machine learning is simply used to find the values of a function’s parameters (coefficients) that minimize a cost function as far as possible.
Detailed explanation-3: -Gradient descent is an optimization algorithm used to find the values of parameters (coefficients) of a function (f) that minimizes a cost function (cost).
Detailed explanation-4: -Gradient descent is used to minimize a cost function J(W) parameterized by a model parameters W. The gradient (or derivative) tells us the incline or slope of the cost function. Hence, to minimize the cost function, we move in the direction opposite to the gradient.