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Difficulty: Medium
Category: Linear Algebra & Machine Learning
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Topics: gradient-descent, learning-rate, optimization, machine-learning
You are training a machine learning model using gradient descent to minimize a cost function $J(\theta)$. The learning rate, denoted by $ \alpha $, controls the step size during each iteration. Consider a scenario where you set the learning rate to a very large value. What is the most likely outcome regarding the convergence of the gradient descent algorithm?
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