Data Science and Machine Learning (Theory and Projects) A to Z - Deep Neural Networks and Deep Learning Basics: converge

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Information Technology (IT), Architecture, Mathematics
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5 questions
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1.
OPEN ENDED QUESTION
3 mins • 1 pt
What are the advantages of using momentum in gradient descent algorithms?
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2.
OPEN ENDED QUESTION
3 mins • 1 pt
What is the significance of adaptive learning rates in speeding up convergence?
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3.
OPEN ENDED QUESTION
3 mins • 1 pt
Why is it difficult to theoretically prove which gradient descent algorithm will perform best on a new dataset?
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4.
OPEN ENDED QUESTION
3 mins • 1 pt
Explain the concept of mini-batches in the context of training neural networks.
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5.
OPEN ENDED QUESTION
3 mins • 1 pt
Discuss the differences between standard regularization techniques and those used in deep neural networks.
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