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Artificial Intelligence
Unit 1
Machine Learning
Introduction to Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Model Evaluation and Validation
Unit 2
Deep Learning
Introduction to Deep Learning
Deep Neural Networks
Training Deep Learning Models
Unit 3
Learning to Rank Models (LLM)
Introduction to Learning to Rank Models
Supervised Learning for LLM
Evaluation Metrics for LLM
Learning to Rank Algorithms
Optimization Techniques for LLM
Unit 1 • Chapter 5
Model Evaluation and Validation
Summary
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Concept Check
What is an important metric in model evaluation?
Accuracy
Learning rate
Loss function
Validation set
In machine learning, what is the purpose of model evaluation?
To assess model performance
To increase training time
To reduce model bias
To select hyperparameters
Why is cross-validation used in model validation?
To speed up model training
To increase model complexity
To overfit the training data
To assess model generalization
What is the main goal of model validation in machine learning?
To ensure model reliability
To expand input features
To memorize training data
To minimize validation loss
Which technique is commonly used for hyperparameter tuning in model validation?
Backpropagation
Batch normalization
Stochastic gradient descent
Grid search
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Reinforcement Learning