hoai_exam2024

Question 1

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Affinity Propagation is a clustering algorithm that finds “exemplars,” or representative data points, to group similar data together. Here’s a simple breakdown:

1.	Idea: Instead of pre-specifying the number of clusters (like k-means), the algorithm lets the data decide. It exchanges “messages” between data points to figure out which points are good representatives (exemplars) for others.

2.	Messages: Two kinds of messages are exchanged:
*	Responsibility: How well-suited a point is to be the exemplar for another.
*	Availability: How appropriate it is for a point to choose another as its exemplar.

3.	Process:
*	Initially, all points can be an exemplar.
*	Messages are updated iteratively, considering the similarity between points.
*	Over time, consensus emerges about which points are exemplars.

4.	Outcome: After a few iterations, each data point is assigned to the closest exemplar, forming clusters.

It’s a bit like a group of people deciding on a leader: everyone votes and exchanges feedback until they agree on a few leaders who best represent them.

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The vanishing gradient problem is an issue that occurs during the training of deep neural networks, particularly in very deep architectures or when using activation functions like sigmoid or tanh. It refers to the situation where gradients become extremely small (or “vanish”) as they are propagated backward through the layers during backpropagation. This makes it difficult for the model to learn effectively.

Question 30

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Dropout is a simple yet powerful tool to make neural networks more robust and less prone to overfitting by introducing randomness during training. Why Is Dropout Useful? * Reduces Overfitting: By randomly disabling neurons, dropout prevents the network from relying too heavily on specific neurons and forces it to learn more general patterns. * Improves Generalization: It encourages the network to develop multiple independent internal representations, making it better at handling unseen data.

Question 31

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Empirical Risk Minimization (ERM) is a fundamental concept in machine learning and statistical learning theory. It refers to the process of optimizing a model to minimize the error (or “risk”) on the training data. In simple terms, it is the strategy of fitting a model by minimizing a loss function based on the observed data.

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Question 33

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Learning rate schedules refer to strategies for adjusting the learning rate during the training of a machine learning model, particularly in neural networks. The learning rate controls how much the model’s weights are updated in response to the computed gradients during each step of training. By modifying the learning rate over time, learning rate schedules aim to improve training stability, speed up convergence, and achieve better model performance.

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Question 35

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A pretrained model refers to a machine learning model that has already been trained on a large dataset for a specific task (or set of tasks) and is then reused or fine-tuned for a different, often related, task. It serves as a starting point for solving new problems, saving time and computational resources.

Question 36

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In machine learning and statistics, regression refers to a type of supervised learning where the goal is to model the relationship between input variables (features) and a continuous output variable (target). In simpler terms, regression is used to predict numeric values based on patterns in the data. **Key Characteristics of Regression** 1. Output is Continuous: Unlike classification (where the output is categorical), regression predicts numeric values, such as prices, temperatures, or probabilities. 2. Goal: The aim is to learn a function f(x) that maps the input features x to the target y , where y is a continuous variable. 3. Loss Function: The most commonly used loss function in regression is the Mean Squared Error (MSE):

Question 37

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In machine learning, hyperparameters are settings or configurations that define the behavior of the learning algorithm and control the training process. Unlike parameters (e.g., weights and biases in a neural network), which are learned during training, hyperparameters are set manually or through automated tuning before the training begins.

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