May 1, 2024
Updated May 11, 2025
29 minute read
Gradient Descent is an optimization algorithm at the core of many machine learning models. Imagine trying to find the lowest point in a foggy valley. You'd likely take a step in the direction that seems to go down the most, then re-evaluate and take another step, continuing until you can't go any lower. Gradient Descent works similarly, iteratively adjusting parameters to minimize a function, typically a "loss" or "cost" function that measures how far off a model's predictions are from the actual values. This process is fundamental to training models that can learn from data and make accurate predictions or decisions.
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Find a path to becoming a Gradient Descent. Learn more at:
OpenCourser.com/topic/pevc6s/gradient
Reading list
We've selected three books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Gradient Descent.
Provides a comprehensive treatment of convex optimization, which powerful technique for solving a wide range of optimization problems. Gradient descent key algorithm for solving convex optimization problems.
Provides a probabilistic perspective on machine learning, which is essential for understanding the theoretical foundations of gradient descent. It covers a wide range of machine learning topics, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of deep learning, which powerful technique for machine learning. Gradient descent key algorithm for training deep learning models.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/pevc6s/gradient