题目

11785/11685/11485 Quiz-MakeUp 1-7

多项选择题

Please refer to Lecture 6 to answer this question. Select all statements that are TRUE:

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标准答案

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思路分析

Starting with the task at hand, we need to evaluate each statement in the list of true/false options. Option 1: "One of the issues in minimization using Newton’s method is that for non-convex functions, the Hessian may not be positive semi-definite because of which the algorithm can diverge." - This aligns with a standard caution in optimization: Newton’s method relies on the Hessian being positive definite (or at least positive semi-definite) to ensure a proper Newton step toward a local minimum. When the Hessian is not PSD, the Newton step can point in a non-desired direction, potentially leading to ascent or divergence, especially in non-convex landscapes. Pract......Login to view full explanation

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类似问题

Which of the following statements about Neural Network (NN) is correct?

Which of the following correctly describes a feedforward neural network? I All of its units, called neurons, in a hidden layer are connected to all of the units in a previous layer. II Its goal is to approximate a function 𝑓 ( 𝑋 ) of the input 𝑋 to get an output 𝑦 . III Feedforward refers to the direction of the information flows through the function being evaluated from 𝑋 up to the output 𝑦 . IV They have feedback connections where the output of a certain layer is fed back to itself, making them the best choice for modeling time series.

What is true regarding the neural network family of algorithms? I Neural Networks can have at most two hidden layers. II Neural Network training involves the computation of several partial derivatives of the error function concerning the model parameters. III Neural Networks can be used as a supervised and an unsupervised method. IV Neural Networks can be used only as a supervised method.

What is true regarding an Autoencoder (AE)? I It is a neural network with a specific structure. II It is an unsupervised learning algorithm. III It is an algorithm to train a regression tree. IV It is a supervised learning algorithm.

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