Gradient Ascent and Descent in Machine Learning

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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?

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?

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?

To obtain the local direction of steepest ascent (A) Signup and view all the answers

What is the goal of using gradient descent in training a neural network?

To minimize the loss function of the model (B) Signup and view all the answers

What is the significance of the gradient vector in gradient descent?

It gives the local direction of steepest descent (C) Signup and view all the answers

What is the purpose of the softmax function in the given context?

To perform normalization to output a probability distribution (D) Signup and view all the answers

What does the expression m(w) represent?

The likelihood of a particular set of weights explaining the observed labels and datapoints (D) Signup and view all the answers

Why is the log-likelihood expression used instead of the likelihood expression?

Because log is an increasing function (C) Signup and view all the answers

What is the difference between a multi-layer perceptron and a multi-layer feedforward neural network?

The type of non-linearity applied (B) Signup and view all the answers

What is the goal of optimizing the weights of a neural network?

To maximize the likelihood of the observed data (A) Signup and view all the answers

What is the advantage of using the log-likelihood expression in mini-batched or stochastic gradient descent?

It is more stable (D) Signup and view all the answers

What is the goal of running gradient ascent on the function m(w)?

To maximize the likelihood of the true class probabilities (C) Signup and view all the answers

What is the main drawback of using batch gradient descent?

It is too slow (A) Signup and view all the answers

What is the purpose of mini-batching?

To speed up gradient descent (C) Signup and view all the answers

What is the limit of mini-batching where the batch size k = 1?

Stochastic gradient descent (SGD) (B) Signup and view all the answers

What is the relation between the number of datapoints in the batch and the computation of gradients?

The number of datapoints in the batch decreases the computation of gradients (B) Signup and view all the answers

What is the goal of updating the parameters w?

To reach a local minimum of the function (C) Signup and view all the answers