Find out why backpropagation and gradient descent are key to prediction in machine learning, then get started with training a simple neural network using gradient descent and Java code.

Conjugate gradient methods form a class of iterative algorithms that are highly effective for solving large‐scale unconstrained optimisation problems.

To machine learning pioneer Terry Sejnowski, the mathematical technique called stochastic gradient descent is the “secret sauce” of deep learning, and most people don’t actually grasp its ...

The Data Science Lab Kernel Ridge Regression with Stochastic Gradient Descent Training Using C# Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression ...

Lagrangian duality and Karush-Kuhn-Tucker conditions. First-order methods and convergence guarantees, including conditional gradient descent and stochastic gradient descent. Quadratic programming, ...

But there are dozens of alternative optimization techniques that don't use gradients. Examples include bio-inspired optimization techniques such as genetic algorithms and particle swarm optimization, ...

DIMITRIS VARTZIOTIS, BENJAMIN HIMPEL, EFFICIENT MESH OPTIMIZATION USING THE GRADIENT FLOW OF THE MEAN VOLUME, SIAM Journal on Numerical Analysis, Vol. 52, No. 2 (2014), pp. 1050-1075 ...