The learning rate is a parameter in optimization algorithms that determines the step size at each iteration while moving towards a minimum of a loss function. It affects how quickly a machine learning model learns and there is a trade-off between convergence rate and overshooting. To achieve faster convergence and prevent getting stuck, the learning rate can be varied during training using a schedule or adaptive methods.
Stanford University
Spring 2022
This is a deep-dive into the details of deep learning architectures for visual recognition tasks. The course provides students with the ability to implement, train their own neural networks and understand state-of-the-art computer vision research. It requires Python proficiency and familiarity with calculus, linear algebra, probability, and statistics.
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