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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 2 • Chapter 3
Training Deep Learning Models
Summary
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Concept Check
What is a key consideration when training deep learning models?
Optimization algorithms
Activation functions
Data preprocessing methods
Regularization techniques
Which technique is used to prevent overfitting in deep learning models?
Dropout
Batch normalization
Weight initialization
Data augmentation
What is the purpose of tuning hyperparameters in deep learning models?
Improving generalization
Optimizing model performance
Reducing model complexity
Speeding up training
Which factor is crucial for successful training of deep learning models?
Quality of training data
Size of the model
Learning rate
Initialization strategy
What is a common challenge faced when training deep learning models?
Convergence issues
Vanishing gradients
Underfitting
Overfitting
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Deep Neural Networks