Search Results for "training"
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54 projects for "training" with 2 filters applied:
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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NVIDIA Isaac GR00T
NVIDIA Isaac GR00T N1.5 is the world's first open foundation model
...It accepts multimodal inputs—such as language and images—and uses a diffusion transformer architecture built upon vision-language encoders, enabling adaptive robot behaviors across diverse environments. It is designed to be customizable via post-training with real or synthetic data. The vision-language model remains frozen during both pretraining and finetuning, preserving language understanding and improving generalization. Streamlined MLP connection between vision encoder and LLM with added layer normalization. -
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Papers We Love
Papers from the computer science community to read and discuss
Papers We Love (PWL) is a global open source community dedicated to reading, discussing, and sharing influential computer science research papers. The repository serves as a curated directory of academic papers that have shaped the field of computing, providing a centralized location for documents that were previously scattered across various online sources. While licensing restrictions prevent hosting all papers directly, PWL offers links to their original sources and clearly marks hosted... -
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TorchQuantum
A PyTorch-based framework for Quantum Classical Simulation
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers. Researchers on quantum algorithm design, parameterized quantum circuit training, quantum optimal control, quantum machine learning, and quantum neural networks. Dynamic computation graph, automatic gradient computation, fast GPU support, batch model terrorized processing. -
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UnsupervisedMT
Phrase-Based & Neural Unsupervised Machine Translation
...Beyond the core EMNLP 2018 setup, the codebase exposes additional, optional capabilities such as multi-language training, language model pretraining with shared parameters, and adversarial training. -
Gemini 3 and 200+ AI Models on One PlatformBuild, govern, and optimize agents and models with Gemini Enterprise Agent Platform.
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BioPPSy
A biochemical property prediction system
...Assembles a set of tools and databases for predicting the physical properties of small molecules. The program models a given property's dependence on a collection of molecular and structural descriptors using a training set of molecules. Neural networks and support vector regression are available, as well as linear models. The models generated by this analysis can then be used to predict the properties of compounds during the development of new and novel drugs. The program and its databases are all open-source. -
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Collaborative Computing Project for NMR
Collaborative Computing Project for NMR (CCPN)
The Collaborative Computational Project for NMR (CCPN) provides tools and knowledge to maximise the impact of the biological NMR studies. The CCPN software facilitates data analysis and software integration. The project actively promotes the exchange of knowledge and provides training and best practices for the NMR community. CCPN also has a leading role in the development of a NMR data-sharing standard and coordination of NMR instrumentation proposals for RCUK and BIS. The 28 partners of CCPN jointly cover all aspects of biomolecular NMR and together they promote excellence in science in their respective fields. -
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Seqping
Gene Prediction Pipeline for Plant Genomes
Gene Prediction Pipeline for Plant Genomes using Self-Training Gene Models and Transcriptomic Data -
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It gives facility of collecting tweets through Twitter Streaming API w.r.t different search criteria and to save tweets in CSV and ARFF (WEKA) file formats.
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Gamera is a framework for the creation of structured document analysis applications by domain experts. It combines a programming library with GUI tools for the training and interactive development of recognition systems.
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simECG is an ECG signal simulator aimed for basic training and experimenting. As a full software ECG signal generator, it's ideal for training and demonstration purposes. You can create customizable signals usually present in many pathologies.
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The General Hidden Markov Model Library (GHMM) is a C library with additional Python bindings implementing a wide range of types of Hidden Markov Models and algorithms: discrete, continous emissions, basic training, HMM clustering, HMM mixtures.
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Adaptive Gaussian Filtering
Machine learning with Gaussian kernels.
Libagf is a machine learning library that includes adaptive kernel density estimators using Gaussian kernels and k-nearest neighbours. Operations include statistical classification, interpolation/non-linear regression and pdf estimation. For statistical classification there is a borders training feature for creating fast and general pre-trained models that nonetheless return the conditional probabilities. Libagf also includes clustering algorithms as well as comparison and validation routines. It is written in C++. -
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The Thot toolkit repository has moved to http://daormar.github.io/thot/ Thot is a toolkit for statistical machine translation. The new Thot toolkit includes fully automatic and interactive machine translation, incremental training of statistical models, parallel estimation, ...
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igafem
Open source 3D Matlab Isogeometric Analysis Code
...The tight coupling of CAD and analysis within IGA requires knowledge from both fields and it is one of the goals of the MIGFEM is to provide a simple-to-understand IGA FEM code. The work is funded under the FP7 Initial Training Network Funding under grant number 289361 'Integrating Numerical Simulation and Geometric Design Technology.' If you use this code or inspired by it please cite the following reference in your work: VP Nguyen, C Anitescu, S Bordas, T Rabczuk. "Isogeometric analysis: an overview and computer implementation aspects". Mathematics and Computers in Simulation, (2015), pp. 89-116, Vol 117. -
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DSOL
DSOL: A Distributed Simulation Object Library implemented in Java
...Since then, numerous papers and PhD dissertations have been developed, with applications in the fields of transportation and traffic, logistics, supply chain management, gaming and training, health care, and many others. -
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Java application for training and deploying text processing applications such as part-of-speech taggers, based on a re-implementation of Brill's algorithm in Java.
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TMVA Toolkit for Multi Variate Analysis
A ROOT-integrated toolkit for multivariate analysis
TMVA is a ROOT-integrated toolkit for multivariate classification and regression analysis. TMVA performs the training, testing and performance evaluation of a large variety of multivariate methods. Since 2013, TMVA has been fully integrated with ROOT and is distributed as part of it. The new homepage of TMVA is https://root.cern -
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MSTParser
MSTParser is a non-projective dependency parser that searches for maxi
MSTParser is a non-projective dependency parser that searches for maximum spanning trees over directed graphs. Models of dependency structure are based on large-margin discriminative training methods. Projective parsing is also supported. mstparser 0.5.1 is now available via Maven Central. If you use Maven as your build tool, then you can add it as a dependency in your pom.xml file: <dependency> <groupId>net.sourceforge.mstparser</groupId> <artifactId>mstparser</artifactId> <version>0.5.1</version> </dependency> -
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cCNN
A fast implementation of LeCun's convolutional neural network
Code of this library is partialy based on myCNN MATLAB class written by Nikolay Chemurin. -
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GPXReport
Analyze gpx files and store summary in Sqlite database
...My favorite one is gps-prune to view gps tracks on a map and make changes. However, I was missing a simple tool to store summary information about a gpx track into a database to keep track of my training efforts. That's why I created GPXReport. GPXReport is a desktop solution which is written in Java and should run on most computers. The program can read a gpx-file and calculate basics information such as: - overall and moving time - distance (km), - average and max speed - time, km uphill and downhill It automatically classifies the acitvity as hiking, MTB or race bike. ... -
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Substitutes the missing GPS information in Garmin TCX files when devices lacking a GPS receiver are used to collect training data (Garmin FR 60, heartrate and pedometer only). GpxTcxWelder integrates GPS info from a GPX v1.1 files into TCX files
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The Self-Organizing Map Maker for Education and Research. Features include 3D-visualization of the training process, various 2D and 3D map topologies, easy extensibility to additional topologies.
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CRFSharp
CRFSharp is a .NET(C#) implementation of Conditional Random Field
...It encodes model parameters by L-BFGS. Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. Currently, when training corpus, compared with CRF++, CRF# can make full use of multi-core CPUs and only uses very low memory, and memory grow is very smoothly and slowly while amount of training corpus, tags increase. with multi-threads process, CRF# is more suitable for large data and tags training than CRF++ now. For example, in machine with 64GB, CRF# encodes model with more than 4.5 hundred million features quickly. -
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The project purpose is to develop an application to store and analyze workout data. Workout data in terms of duration of a training session, average pulse during the session and so forth. The focus will be on endurance training.
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This is a MATLAB implementation of inverse compositional Active Appearance Models (AAMs), as described in the "Active Appearance Models Revisited" paper by Iain Matthews and Simon Baker.
