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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 2
Deep Neural Networks
Summary
False
Concept Check
What is a key component of Deep Neural Networks?
Activations
Weights
Biases
Neurons
Which type of layers typically exist in Deep Neural Networks?
Input layers
Dense layers
Output layers
Hidden layers
What is the purpose of activation functions in Deep Neural Networks?
Perform matrix multiplication
Introduce non-linearity
Calculate loss function
Regularize the model
What is backpropagation used for in Deep Neural Networks?
Update weights
Compute accuracy
Normalize data
Summarize features
What is the goal of training a Deep Neural Network?
Avoid overfitting
Increase learning rate
Maximize number of layers
Minimize loss function
Check Answer
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Introduction to Deep Learning
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Training Deep Learning Models