Gradient descent is an optimization algorithm used to find the minimum of a cost function by iteratively adjusting parameters in the direction of steepest descent. It's like hiking down a mountain by always taking the steepest path, except instead of a mountain it's a high-dimensional cost landscape, and instead of hiking you're updating weights in a neural network or coefficients in a regression model.

How to use it in a sentence

• I've been battling this gradient descent algorithm all day, but it keeps getting stuck in local minima like a tent stake in rocky ground.

• My manager keeps asking when the model will be done, but I can't exactly put a deadline on gradient descent - it's not like I can just tell it to "optimize faster, damn it!"

If you actually want to learn more...

• An overview of gradient descent optimization algorithms: This article provides a comprehensive overview of various gradient descent optimization algorithms, including batch, stochastic, and mini-batch variants, as well as more advanced techniques like Momentum, Adagrad, and Adam.

• Gradient Descent For Machine Learning: This tutorial explains the fundamentals of gradient descent in the context of machine learning, with examples of how it's used to optimize different types of models.

• Gradient Descent, Step-by-Step: This video by 3Blue1Brown provides an intuitive, visual explanation of how gradient descent works, building up from simple examples to more complex applications in machine learning.

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