Python for Deep Learning - Build Neural Networks in Python - What is Stochastic Gradient Descent?

The video tutorial discusses the limitations of gradient descent in nonconvex functions due to multiple local minima. It introduces stochastic gradient descent (SGD) as an alternative, highlighting its randomness and efficiency in training time. The tutorial also explains mini-batch gradient descent, which uses a subset of data, offering a balance between SGD and traditional gradient descent.

What is the main limitation of gradient descent when dealing with nonconvex functions?

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Explain the concept of stochastic gradient descent and how it differs from traditional gradient descent.

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How does the randomness in stochastic gradient descent affect the path taken to reach the minima?

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Discuss the advantages of using stochastic gradient descent over traditional gradient descent.

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What is mini-batch gradient descent and how does it relate to stochastic gradient descent?

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