MACHINE LEARNING | Dezhi Yu

Generative Modeling of Bach-Style Symbolic Music: A Comparative Study of Autoregressive, Latent-Variable, and Adversarial Approaches

Mon, 01 Jun 2026 00:00:00 +0000

AdaMixup: A Dynamic Defense Framework for Membership Inference Attack Mitigation

Wed, 01 Jan 2025 00:00:00 +0000

Machine Learning Optimizes the Efficiency of Picking and Packing in Automated Warehouse Robot Systems

Wed, 01 Jan 2025 00:00:00 +0000

How to understand gradient descent?

Sun, 21 Oct 2018 10:05:36 +0000

Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. To find a local minimum of a function using gradient descent, we take steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point. But if we instead take steps proportional to the positive of the gradient, we approach a local maximum of that function; the procedure is then known as gradient ascent. Gradient descent is generally attributed to Cauchy, who first suggested it in 1847, but its convergence properties for non-linear optimization problems were first studied by Haskell Curry in 1944.

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