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Questions and Answers
What is the purpose of gradient ascent in maximizing the log-likelihood function?
What is the purpose of gradient ascent in maximizing the log-likelihood function?
- To update the parameters in the direction of the gradient (correct)
- To compute the partial derivatives of the function
- To minimize the function
- To find the local direction of steepest descent
What is the update rule for gradient descent?
What is the update rule for gradient descent?
- w ← w * α∇w f(w)
- w ← w - α∇w f(w) (correct)
- w ← w + α∇w f(w)
- w ← w / α∇w f(w)
What is the role of the step size α in gradient ascent?
What is the role of the step size α in gradient ascent?
- It determines the learning rate of the algorithm (correct)
- It determines the accuracy of the model
- It determines the number of iterations required
- It determines the complexity of the model
What is the purpose of computing the gradient vector?
What is the purpose of computing the gradient vector?
What is the goal of using gradient descent in training a neural network?
What is the goal of using gradient descent in training a neural network?
What is the significance of the gradient vector in gradient descent?
What is the significance of the gradient vector in gradient descent?
What is the purpose of the softmax function in the given context?
What is the purpose of the softmax function in the given context?
What does the expression m(w) represent?
What does the expression m(w) represent?
Why is the log-likelihood expression used instead of the likelihood expression?
Why is the log-likelihood expression used instead of the likelihood expression?
What is the difference between a multi-layer perceptron and a multi-layer feedforward neural network?
What is the difference between a multi-layer perceptron and a multi-layer feedforward neural network?
What is the goal of optimizing the weights of a neural network?
What is the goal of optimizing the weights of a neural network?
What is the advantage of using the log-likelihood expression in mini-batched or stochastic gradient descent?
What is the advantage of using the log-likelihood expression in mini-batched or stochastic gradient descent?
What is the goal of running gradient ascent on the function m(w)?
What is the goal of running gradient ascent on the function m(w)?
What is the main drawback of using batch gradient descent?
What is the main drawback of using batch gradient descent?
What is the purpose of mini-batching?
What is the purpose of mini-batching?
What is the limit of mini-batching where the batch size k = 1?
What is the limit of mini-batching where the batch size k = 1?
What is the relation between the number of datapoints in the batch and the computation of gradients?
What is the relation between the number of datapoints in the batch and the computation of gradients?
What is the goal of updating the parameters w?
What is the goal of updating the parameters w?
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