Gradient descent algorithm
• Minimize cost function
• Gradient descent is used many minimization problems
• For a given cost function, cost (W, b), it will find W, b to minimize cost
• It can be applied to more general function: cost (w1, w2, …)
How it works?
• Start with initial guesses - Start at 0,0 (or any other value)
- Keeping changing W and b a little bit to try and reduce cost(W, b)
• Each time you change the parameters, you select the gradient which reduces cost(W, b) the most possible
• Repeat
• Do so until you converge to a local minimum
• Has an interesting property
- Where you start can determine which minimum you end up
출처 - https://www.youtube.com/channel/UCML9R2ol-l0Ab9OXoNnr7Lw
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