Recurrent Neural Networks can be trained to solve various types of tasks. Which

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Recurrent Neural Networks can be trained to solve various types of tasks. Which of the following is not an example of such a task? Predicting the price stock on a certain day based on the values of economic variables in the past Translating text in other language to text in another language Classification of human action based on the data read from sensors over time of an Classification of an image bated on the object visible in the foreground of the image Which of these is not a stationary point of a function f in numerical optimization? The point where f = 0 The point where f is minimum The point where f maximum A saddle point for f For a function g of a single variable v. the function g of a single variable v, the function a is convex if and only if the following inequality involving the second derivative of g is true. (True/False) g"(v) greaterthanorequalto 0 Which of the following is a commonly used stopping condition for gradient descent algorithms? The error exceeds a certain value The error falls below a certain value The gradient falls below a certain value The gradient exceeds a certain value What is the difference between gradient descent and Newton's method for finding the minima of a function? Gradient descent uses the linear approximation whereas Newton's method uses the quadratic approximation of the function Gradient descent uses the quadratic approximation whereas Newton's method uses the linear approximation of the function. Gradient descent can get stuck in a local minimum whereas Newton's method will never get stuck in a local minimum. Gradient descent works best for first order function whereas Newton's method works better for higher order functions In regression the goal is to minimize the least squares cost function shown below. Sigma^P_p = 1 (b + x^T_p w - y_p)^2

Explanation / Answer

8 - predicting the price of a stock on a certain day based on the value of economic variables in the past

9 - the point where f=0

11 - The gradient falls below a certain value

12 - gradient descent uses the linear approximation where as newtons method uses the quadratic approximation of the function

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