1,639 skills found · Page 15 of 55
AlgebraicJulia / Petri.jlA Petri net modeling framework for the Julia programming language
hopv / MoCHiMoCHi: Model Checker for Higher-Order Programs
erich666 / T2ZProcessing program that creates GIF animations and converts these to 3D printable models.
sergev / Mk 61Replica of MK-61 programmable calculator is based on a cycle-accurate model of legacy ICs running on a modern microcontroller.
yaof20 / ReaLImplementation and datasets for "Training Language Models to Generate Quality Code with Program Analysis Feedback"
KMarkert / Servir Vic TrainingTraining materials for the VIC hydrologic model setup developed for the @SERVIR program
People-Places-Solutions / Floodmodeller ApiThe Flood Modeller Python API is a free and open-source python package which provides a bridge between Flood Modeller and the python programming language to extend the capabilities of Flood Modeller in automated workflows
tdcosim / SolarPV DER Simulation ToolAllows user to run dynamics simulations for solar photovoltaic distributed energy resource connected to a stiff voltage source or to an external program. It allows modifying DER parameters, introducing external disturbance events, and visualizing the simulation results. The PV-DER (inverter) is modeled using dynamic phasor concept.
TerraME / TerrameTerraME is a programming environment for spatial dynamical modelling
mnielsen / VSMToy Python program illustrating the vector space model of documents
UM-Bridge / UmbridgeUM-Bridge (the UQ and Model Bridge) provides a unified interface for numerical models that is accessible from virtually any programming language or framework.
SAP-archive / Cloud Cap MultitenancySAP Cloud Application Programming Model (CAP) sample code project with multitenancy using service manager-created SAP HANA containers for tenant data isolation.
alejandrosantanabonilla / PysoftkPython program for modelling and simulating polymers.
abdelrahmaan / Machine Learning Scientist With Python By DataCampPython programming skill set with the toolbox to perform supervised, unsupervised, and deep learning, learn how to process data for features, train your models, assess performance, and tune parameters for better performance. natural language processing, image processing, and popular libraries such as Spark and Keras.
grvlbit / THERMAIDThermo-Hydraulic Energy Resource Modelling for Application and Development and is a MATLAB program solving flow and heat transport in fractured porous media using the embedded discrete fracture method
Opt-Mucca / PySCIPOpt MLPython interface to automatically formulate Machine Learning models into Mixed-Integer Programs
vrdmr / CS273a Introduction To Machine LearningIntroduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".
imuncle / TSCM CalibThe calibration program with Double Sphere Camera Model
jmsallan / LinearprogrammingCode for the Modeling and Solving Linear Programming with R book
open-optimization / Open Optimization Or BookMathematical Programming and Operations Research: Modeling, Algorithms, and Complexity. Examples in Python and Excel. Edited by Robert Hildebrand
