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What can be the disadvantage if the learning rate is very high in gradient descent?
If your learning rate is set too low, training will progress very slowly as you are making very tiny updates to the weights in your network. However, if your learning rate is set too high, it can cause undesirable divergent behavior in your loss function.
Under what values of the learning rate does gradient descent converge?
Here, you have to know that it has already been established for GD, GD with momentum and SGD, that for any optimization problem, gradient descent converges to a local minimizer if the learning rate is less than 1/L, where L is the Lipschitz smoothness of the loss function with respect to the parameters.
Why is the learning rate important in gradient descent?
The learning rate gives you control of how big (or small) the updates are going to be. A bigger learning rate means bigger updates and, hopefully, a model that learns faster. But th e re is a catch, as always… if the learning rate is too big, the model will not learn anything. This leads us to two fundamental questions:
How is stochastic gradient descent used in deep learning?
Stochastic gradient descent is an optimization algorithm that estimates the error gradient for the current state of the model using examples from the training dataset, then updates the weights of the model using the back-propagation of errors algorithm, referred to as simply backpropagation.
How to calculate model’s error in gradient descent?
A popular and easy-to-use technique to calculate those parameters is to minimize model’s error with Gradient Descent. The Gradient Descent estimates the weights of the model in many iterations by minimizing a cost function at every step.
When do you skip the optimal solution in gradient descent?
If the λ is very large we will skip the optimal solution. If it is too small we will need too many iterations to converge to the best values. So using a good λ is crucial.