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Hi! Can you explain gradient descent in machine learning?

Of course! Gradient descent is an optimization algorithm used to minimize the loss function in machine learning models.

Core Idea:

  1. Calculate the gradient (derivative) of the loss function with respect to parameters
  2. Update parameters in the opposite direction of the gradient
  3. Repeat this process until convergence

Formula: θ = θ - α × ∇J(θ)

Where:

  • θ is the parameter
  • α is the learning rate
  • ∇J(θ) is the gradient

Would you like to see a specific implementation example?

How should I choose the learning rate?

Choosing the learning rate is crucial!

Selection Principles:

  • Too large: May cause oscillation, fail to converge
  • Too small: Converges too slowly, long training time
  • Just right: Smooth and fast convergence

Common Strategies:

  1. Start with 0.001 or 0.01
  2. Use learning rate decay (gradually decrease)
  3. Use adaptive learning rate algorithms (Adam, RMSprop)

Would you like me to recommend some learning resources?

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