Gradient Descent
An optimization method that iteratively updates model parameters in the opposite direction of the gradient
#Gradient Descent#optimization algorithm#parameter updates#learning rate
What is gradient descent?
Gradient descent is an optimization method used to minimize model loss by updating parameters step by step.
It moves parameters in the opposite direction of the gradient to reduce error.
What variants are common?
Batch, stochastic, and mini-batch versions are standard training patterns.
In practice, adaptive optimizers such as Adam and RMSprop are often used as related extensions.
Why does it matter?
Gradient descent strongly affects training stability and convergence speed.
Poor learning-rate settings can cause slow progress or divergence.
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