What is the main goal of adjusting parameters in a machine learning algorithm?
Deep Learning CNN Convolutional Neural Networks with Python - Gradient Descent

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2.
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Explain how the gradient direction affects the loss in a machine learning model.
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What is the significance of the step size in the gradient descent algorithm?
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How does the gradient descent algorithm ensure that the loss does not increase?
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Describe the process of updating parameters in gradient descent.
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What happens to the loss function if it is convex when using gradient descent?
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In what scenarios might other optimization algorithms be preferred over gradient descent?
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