What is momentum based gradient descent?
Gradient descent is an optimization algorithm that uses the gradient of the objective function to navigate the search space. Gradient descent can be accelerated by using momentum from past updates to the search position.
What is vanilla gradient descent?
Vanilla gradient descent means the basic gradient descent algorithm without any bells or whistles. There are many variants on gradient descent. In usual gradient descent (also known as batch gradient descent or vanilla gradient descent), the gradient is computed as the average of the gradient of each datapoint.
How is the momentum method different from Nesterov accelerated gradient?
The distinction between Momentum method and Nesterov Accelerated Gradient updates was shown by Sutskever et al. in Theorem 2.1, i.e., both methods are distinct only when the learning rate η is reasonably large.
What’s the difference between momentum based gradient and gradient descent?
Here v is velocity aka step aka state, and mu is a momentum factor, typically 0.9 or so. ( v, x and learning_rate can be very long vectors; with numpy, the code is the same.) v in the first line is gradient descent with momentum; v_nesterov extrapolates, keeps going.
What is beta for stochastic gradient descent with momentum?
Exponentially weighed averages define a new sequence V with the following equation: That sequence V is the one plotted yellow above. Beta is another hyper-parameter which takes values from 0 to one. I used beta = 0.9 above. It is a good value and most often used in SGD with momentum. Intuitively, you can think of beta as follows.
Can a momentum method be used for noisy gradients?
Noisy gradients can be a problem too since Stochastic Gradient Descent will frequently follow the wrong gradient. In neural networks training, Momentum method can be used to mitigate these problems and accelerate learning compared to plain Stochastic Gradient Descent.