Which optimization algorithm adapts the learning rate for each parameter based on its gradient history?
Question
Which optimization algorithm adapts the learning rate for each parameter based on its gradient history?
Solution
The optimization algorithm that adapts the learning rate for each parameter based on its gradient history is called "Adaptive Moment Estimation" or "Adam".
Here's a step-by-step explanation of how it works:
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Initialize the parameters: Adam starts with an initial estimate of the parameters of the model.
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Compute the gradient: In each iteration, Adam computes the gradient of the loss function with respect to the parameters.
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Update biased first and second moment estimates: Adam maintains an exponentially decaying average of past gradients and squared gradients.
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Correct bias in moment estimates: Adam corrects the bias in its estimates to handle the initialization at the origin.
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Compute parameter update: Adam computes the update of the parameters based on the corrected estimates.
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Apply the update: Finally, Adam applies the computed update to the parameters.
This process is repeated until the algorithm converges to the optimal parameters. The adaptive learning rate for each parameter makes Adam particularly effective when dealing with sparse gradients or noisy data.
Similar Questions
The algorithm is known for its efficient computational performance for large datasets by approximating the gradient of the cost function on smaller batches. On the other hand, the algorithm adapts the learning rate for each parameter by considering the recent magnitude of the gradients, helping in faster convergence, especially when dealing with data.
Gradient Descent is an optimization algorithm used for ______
What is the RProp algorithm's learning rate update rule?Question 16Answera.The learning rate is updated based on the difference between the current and previous iteration's errorb.The learning rate is updated based on the difference between the current and previous iteration's weightsc.The learning rate is updated based on the difference between the current and previous iteration's gradientd.The learning rate is updated based on the difference between the current and previous iteration's Hessian matrix
What is the purpose of the learning rate in neural network optimization?Review LaterTo control the number of epochsTo determine the batch sizeTo adjust the step size during weight updatesTo set the initial weights
In Gradient Descent, refers to the magnitude of updates to the parameters, and refers to the direction of updates.
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