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Which of the following requires that the entire training dataset is in memory and available to the algorithm?
Commonly, batch gradient descent is implemented in such a way that it requires the entire training dataset in memory and available to the algorithm.
What is dropout rate in deep learning?
Dropout is a technique where randomly selected neurons are ignored during training. This means that their contribution to the activation of downstream neurons is temporally removed on the forward pass and any weight updates are not applied to the neuron on the backward pass.
What are the disadvantages of Batch Gradient descent?
In batch gradient descent, you compute the gradient over the entire dataset, averaging over potentially a vast amount of information. It takes lots of memory to do that. But the real handicap is the batch gradient trajectory land you in a bad spot (saddle point).
How is mini-batch gradient descent used in deep learning?
Mini-batch gradient descent seeks to find a balance between the robustness of stochastic gradient descent and the efficiency of batch gradient descent. It is the most common implementation of gradient descent used in the field of deep learning.
Is the gradient of cost function intractable in gradient descent?
As we need to calculate the gradient on the whole dataset to perform just one update, batch gradient descent can be very slow and is intractable for datasets that don’t fit in memory. After initializing the parameter with arbitrary values we calculate gradient of cost function using following relation:
How is stochastic gradient descent used in machine learning?
Stochastic gradient descent, often abbreviated SGD, is a variation of the gradient descent algorithm that calculates the error and updates the model for each example in the training dataset. The update of the model for each training example means that stochastic gradient descent is often called an online machine learning algorithm.