PyBrain is a modular Machine Learning Library for Python. It's goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms.
PyBrain is short for Python-Based Reinforcement Learning
... [More], Artificial Intelligence and Neural Network Library.
It's the Swiss army knife for machine learning and neural networking. [Less]
Geneva is a library written in C++ for performing parametric optimization in parallel on devices ranging from multi-processor machines over clusters to Grids and Cloud installations. Geneva currently supports Evolutionary Algorithms, Swarm Algorithms, Gradient Descents, a form of Simulated Annealing
... [More] as well as Parameter Scans. All algorithms act on the same data structures for the description of optimization problems, so that it becomes possible to "chain" different algorithms, making the result of one algorithm the input of another. [Less]
Nelder-Mead is an algorithm that I have used on many occasions over the years. It is a good general purpose gradient descent algorithm for optimizing non-linear multivariate equations, and does not require differentiability. I couldn't find a free C# implementation, so I wrote one.
Program to estimate material properties from the measured first order thickness resonance in the ultrasonic transmission coefficient of a homogeneous plate at normal incidence.
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