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In the Actor-Critic model, what role does the Critic's feedback play in adjusting the Actor's policies?

  • Evaluating policy
  • Selecting actions
  • Providing rewards
  • Discovering optimal actions
The Critic in the Actor-Critic model evaluates the current policy by estimating the value function. This evaluation helps the Actor make better decisions by guiding it towards actions that result in higher expected rewards, ultimately improving the policy.
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In a DQN, the primary function of the neural network is to approximate which function?

  • State-Action Value Function
  • Policy Function
  • Environment Dynamics Function
  • Reward Function
The primary role of the neural network in a Deep Q Network (DQN) is to approximate the State-Action Value Function (Q-function).
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In K-means clustering, the value of K represents the number of ________.

  • Clusters
  • Data Points
  • Features
  • Centroids
In K-means clustering, 'K' represents the number of clusters you want to partition your data into. Each cluster will have its centroid.
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n the context of CNNs, why are pooling layers important despite them leading to a loss of information?

  • Pooling layers help reduce the spatial dimensions, aiding in computation
  • Pooling layers introduce non-linearity and increase model complexity
  • Pooling layers reduce the number of filters in the network
  • Pooling layers improve interpretability of features
Pooling layers are crucial for dimensionality reduction, making computations feasible, and for creating translation-invariant features. Despite information loss, it retains the most essential features.
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What is the primary reason for using Random Forests over a single Decision Tree in many applications?

  • Faster training time
  • Increased accuracy
  • Lower memory usage
  • Simplicity
Random Forests are preferred due to their increased accuracy over single Decision Trees. They work by aggregating the predictions of multiple trees, which reduces overfitting and results in better overall performance.
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In which learning approach does the model learn to...

  • Reinforcement Learning
  • Semi-Supervised Learning
  • Supervised Learning
  • Unsupervised Learning
In reinforcement learning, a model learns by interacting with an environment and receiving rewards or penalties based on its actions. It aims to make decisions to maximize cumulative rewards.
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The value at which the sigmoid function outputs a 0.5 probability, thereby determining the decision boundary in logistic regression, is known as the ________.

  • Decision Point
  • Inflection Point
  • Sigmoid Threshold
  • Threshold Value
The value at which the sigmoid function outputs a 0.5 probability is known as the decision point. This is the threshold value that separates the two classes in a binary logistic regression.
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You are working on a dataset with a large number of features. While some of them seem relevant, many appear to be redundant or irrelevant. What technique would you employ to enhance model performance and interpretability?

  • Data Normalization
  • Feature Scaling
  • Principal Component Analysis (PCA)
  • Recursive Feature Elimination (RFE)
Principal Component Analysis (PCA) is a dimensionality reduction technique that can help reduce the number of features while preserving the most important information. It enhances model performance by eliminating redundant features and improves interpretability by transforming the data into a new set of uncorrelated variables.
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In reinforcement learning, the agent learns a policy which maps states to ________.

  • Actions
  • Rewards
  • Values
  • Policies
In reinforcement learning, the agent learns a policy that maps states to optimal actions, hence filling in the blank with "Policies" is accurate. This policy helps the agent make decisions in various states.
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A robot is navigating a maze. Initially, it often runs into walls or dead-ends, but over time it starts finding the exit more frequently. To achieve this, the robot likely emphasized ________ in the beginning and shifted towards ________ over time.

  • Exploration, Exploitation
  • Breadth-First Search
  • Depth-First Search
  • A* Search
In the context of reinforcement learning, the robot employs "exploration" initially to discover the maze, and as it learns, it shifts towards "exploitation" to choose actions that yield higher rewards, like finding the exit.
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Dimensionality reduction techniques, like PCA and t-SNE, are essential when dealing with the ________ curse.

  • Overfitting
  • Bias-Variance Tradeoff
  • Curse of Dimensionality
  • Bias
The "Curse of Dimensionality" refers to the increased complexity and sparsity of data in high-dimensional spaces. Dimensionality reduction techniques, such as PCA (Principal Component Analysis) and t-SNE, are crucial to mitigate the adverse effects of this curse.
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Consider a robot that learns to navigate a maze. Instead of learning the value of each state or action, it tries to optimize its actions based on direct feedback. This approach is most similar to which reinforcement learning method?

  • Monte Carlo Methods
  • Temporal Difference Learning (TD)
  • Actor-Critic Method
  • Q-Learning
In this context, the robot is optimizing actions based on direct feedback, which is a characteristic of the Actor-Critic method. This method combines value-based and policy-based approaches, making it similar to the situation described.
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