Gradient Descent With Momentum from Scratch
Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A problem with gradient descent is that it can bounce around the search space on optimization problems that have large amounts of curvature or noisy gradients, and it can get stuck in flat spots in the search space that have no gradient. Momentum is an extension to the gradient descent optimization algorithm that allows the search to build inertia in a direction in the search space and overcome the oscillations of noisy gradients and coast across flat spots of the search space. In this tutorial, you will discover the gradient descent with momentum algorithm. After completing this tutorial, you will know: Kick-start your project with my new book Optimization for Machine Learning, including step-by-step tutorials and the Python source code files for all examples. Gradient Descent With Momentum from Scratch Photo by Chris Barnes, some ri
By Jason Brownlee on October 12, 2021 in Optimization 16 Share Post Share Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A problem with gradient descent is that it can bounce around the search space on optimization problems that have large amounts of curvature or noisy gradients, and it can get stuck in flat spots in the search space that have no gradient. Momentum is an extension to the gradient descent optimization algorithm that allows the search to build inertia in a direction in the
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