Contents
What is momentum in gradient descent?
Momentum is an extension to the gradient descent optimization algorithm that allows the search to build inertia in a direction in the search space and overcome the oscillations of noisy gradients and coast across flat spots of the search space.
Does momentum speed up training?
When training a neural network, you must experiment with different momentum factor values. In some situations, using no momentum (or equivalently, a momentum factor of 0.0) leads to better results than using momentum. However, in most scenarios, using momentum gives you faster training and better predictive accuracy.
What is momentum in Adam Optimizer?
Adam optimizer involves a combination of two gradient descent methodologies: Momentum: This algorithm is used to accelerate the gradient descent algorithm by taking into consideration the ‘exponentially weighted average’ of the gradients. Using averages makes the algorithm converge towards the minima in a faster pace.
Is RMSprop adaptive?
RMSprop is an unpublished, adaptive learning rate method proposed by Geoff Hinton in Lecture 6e of his Coursera Class. RMSprop as well divides the learning rate by an exponentially decaying average of squared gradients. Hinton suggests γ to be set to 0.9, while a good default value for the learning rate η is 0.001.
Does learning rate matter for Adam?
Even in the Adam optimization method, the learning rate is a hyperparameter and needs to be tuned, learning rate decay usually works better than not doing it.
What’s the problem with the simplest RNN model?
That is because the simplest RNN model has a major drawback, called vanishing gradient problem, which prevents it from being accurate. In a nutshell, the problem comes from the fact that at each time step during training we are using the same weights to calculate y_t.
Why are RNN networks not hardware friendly?
And one issue of RNN is that they are not hardware friendly. Let me explain: it takes a lot of resources we do not have to train these network fast. Also it takes much resources to run these model in the cloud, and given that the demand for speech-to-text is growing rapidly, the cloud is not scalable.
Why do vanishing gradients occur in RNN / LSTM?
This arrow means that long-term information has to sequentially travel through all cells before getting to the present processing cell. This means it can be easily corrupted by being multiplied many time by small numbers < 0. This is the cause of vanishing gradients.
How are LSTM and RNN used in data science?
One could then better understand that LSTM were a clever bypass technique. Also attention showed that MLP network could be replaced by averaging networks influenced by a context vector. More on this later. It only took 2 more years, but today we can definitely say: