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Super Kai - Kazuya Ito
Super Kai - Kazuya Ito
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Mini-Batch Gradient Descent(MBGD):

  • Pros:
    • It's good at a large dataset while BGD is not.
    • It's good at online learning while BGD is not. *Online learning is the way which a model incrementally learns from a stream of dataset in real-time.
    • It doesn't need the repreparation of a whole dataset if you want to update a model while BGD needs.
    • It less gets stuck incan more easily escape local minima or saddle points than BGD.
  • Cons:
    • The computation is less stable than BGD.
    • It's less strong in noise(noisy data) than BGD.
    • It gets a less accurate value than BGD.

Mini-Batch Gradient Descent(MBGD):

  • Pros:
    • It's good at a large dataset while BGD is not.
    • It's good at online learning while BGD is not. *Online learning is the way which a model incrementally learns from a stream of dataset in real-time.
    • It doesn't need the repreparation of a whole dataset if you want to update a model while BGD needs.
    • It less gets stuck in local minima or saddle points than BGD.
  • Cons:
    • The computation is less stable than BGD.
    • It's less strong in noise(noisy data) than BGD.
    • It gets a less accurate value than BGD.

Mini-Batch Gradient Descent(MBGD):

  • Pros:
    • It's good at a large dataset while BGD is not.
    • It's good at online learning while BGD is not. *Online learning is the way which a model incrementally learns from a stream of dataset in real-time.
    • It doesn't need the repreparation of a whole dataset if you want to update a model while BGD needs.
    • It can more easily escape local minima or saddle points than BGD.
  • Cons:
    • The computation is less stable than BGD.
    • It's less strong in noise(noisy data) than BGD.
    • It gets a less accurate value than BGD.
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Source Link
Super Kai - Kazuya Ito
Super Kai - Kazuya Ito
  • 165
  • 7

Mini-Batch Gradient Descent(MBGD):

  • Pros::
    • It's good at a large dataset while BGD is not.
    • It's good at online learning while BGD is not. *Online learning is the way which a model incrementally learns from a stream of dataset in real-time.
    • It doesn't need the repreparation of a whole dataset if you want to update a model while BGD needs.
    • It less gets stuck in local minima or saddle points than BGD.
  • Cons::
    • The computation is less stable than BGD.
    • It's less strong in noise(noisy data) than BGD.
    • It gets a less accurate value than BGD.

Mini-Batch Gradient Descent(MBGD):

  • Pros:
    • It's good at a large dataset while BGD is not.
    • It's good at online learning while BGD is not. *Online learning is the way which a model incrementally learns from a stream of dataset in real-time.
    • It doesn't need the repreparation of a whole dataset if you want to update a model while BGD needs.
    • It less gets stuck in local minima or saddle points than BGD.
  • Cons:
    • The computation is less stable than BGD.
    • It's less strong in noise(noisy data) than BGD.
    • It gets a less accurate value than BGD.

Mini-Batch Gradient Descent(MBGD):

  • Pros:
    • It's good at a large dataset while BGD is not.
    • It's good at online learning while BGD is not. *Online learning is the way which a model incrementally learns from a stream of dataset in real-time.
    • It doesn't need the repreparation of a whole dataset if you want to update a model while BGD needs.
    • It less gets stuck in local minima or saddle points than BGD.
  • Cons:
    • The computation is less stable than BGD.
    • It's less strong in noise(noisy data) than BGD.
    • It gets a less accurate value than BGD.
added 259 characters in body
Source Link
Super Kai - Kazuya Ito
Super Kai - Kazuya Ito
  • 165
  • 7

The pros of Mini-Batch Gradient Descent(MBGD):

  • It's good at a large dataset while BGD is not.
  • It's good at online learning. *Online learning is the way which a model incrementally learns from a stream of dataset in real-time while BGDPros: is not.
    • It's good at a large dataset while BGD is not.
    • It's good at online learning while BGD is not. *Online learning is the way which a model incrementally learns from a stream of dataset in real-time.
    • It doesn't need the repreparation of a whole dataset if you want to update a model while BGD needs.
    • It less gets stuck in local minima or saddle points than BGD.
  • It doesn't need the repreparation of a whole dataset if you want to update a model while BGDCons: needs.
  • It less gets stuck in local minima or saddle points than BGD.
    • The computation is less stable than BGD.
    • It's less strong in noise(noisy data) than BGD.
    • It gets a less accurate value than BGD.

But the cons is that MBGD outputs a less accurate value than BGD.

The pros of Mini-Batch Gradient Descent(MBGD):

  • It's good at a large dataset while BGD is not.
  • It's good at online learning. *Online learning is the way which a model incrementally learns from a stream of dataset in real-time while BGD is not.
  • It doesn't need the repreparation of a whole dataset if you want to update a model while BGD needs.
  • It less gets stuck in local minima or saddle points than BGD.

But the cons is that MBGD outputs a less accurate value than BGD.

Mini-Batch Gradient Descent(MBGD):

  • Pros:
    • It's good at a large dataset while BGD is not.
    • It's good at online learning while BGD is not. *Online learning is the way which a model incrementally learns from a stream of dataset in real-time.
    • It doesn't need the repreparation of a whole dataset if you want to update a model while BGD needs.
    • It less gets stuck in local minima or saddle points than BGD.
  • Cons:
    • The computation is less stable than BGD.
    • It's less strong in noise(noisy data) than BGD.
    • It gets a less accurate value than BGD.
added 259 characters in body
Source Link
Super Kai - Kazuya Ito
Super Kai - Kazuya Ito
  • 165
  • 7
Source Link
Super Kai - Kazuya Ito
Super Kai - Kazuya Ito
  • 165
  • 7