numericalalgorithmsgroup/AzureML_Best_Practice

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numericalalgorithmsgroup/AzureML_Best_Practice

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Reference Implementations of AzureML Workloads with Cost/Performance Best Practice

pybobyqa

Python-based Derivative-Free Optimization with Bound Constraints

NAGPythonExamples

Examples and demos showing how to call functions from the NAG Library for Python

Jupyter Notebook

LAPACK_Examples

Example programs showing how to call LAPACK driver and computational routines (Fortran double precision)

dfols

Python-based Derivative-Free Optimizer for Least-Squares

dfogn

DFO-GN: Derivative-Free Optimization using Gauss-Newton

nagcpp

C++ Interfaces for the NAG Library

2decomp_fft

pypop

Python Tools for the POP Metrics

Jupyter Notebook

Ski-LLS

dfbgn

Python solver for large-scale nonlinear least-squares minimization without derivatives

HighPerformancePythonTraining

Jupyter Notebook

NAGPythonLibraryTraining

Hands on exercises and solutions for training in the NAG Library for Python

Jupyter Notebook

AlgorithmicDifferentiation

NAGJavaExamples

Examples demonstrating the NAG Numerical Library for Java

robinhood-reports

A set of reports for presenting data collected by the Robinhood Policy Engine

dco_cpp

MLFirstSteps_Azure

Tutorial demonstrating how to get started porting an existing Machine Learning application to MS Azure

bayesian-uq

NAGPython_PyQ

Using the NAG Library for Python with Kdb+ and PyQ

The-Role-of-Matrix-Functions

A Jupyter Notebook about using matrix functions with the NAG Library for Python

Jupyter Notebook

kdb_ffi

performance_profiling_examples

Accompanying resources for the NAG webinar and blog series