high performance r package machine learning

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  • Top R Packages for Machine Learning - KDnuggets

    2021-5-17 · The h2o4gpu R package is a wrapper around the h2o4gpu Python package. The R package makes use of RStudio's reticulate package for facilitating access to Python libraries through R. Reticulate embeds a Python session within your R session, enabling seamless, high-performance interoperability.

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  • H2O4GPU: Machine Learning with GPUs in R

    2020-3-24 · 3rd High Performance Machine Learning Workshop. NEWS: Due to Covid-19 CCGrid and HPML2020 is posponed to November 2-5 2020 May 11th, 2020 November 2nd, 2020, Melbourne - Australia. Held in conjunction with IEEE …

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  • 5 Reasons to Learn H2O for High-Performance

    xLearn - High-Performance and Scalable Machine Learning Package.

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  • HPML 2020: HIGH PERFORMANCE MACHINE

    2018-4-6 · The package is an extension to the h2o R package that allows the user to train an ensemble in the H2O cluster using any of the supervised machine learning algorithms H2O. Features: Uses data-distributed and parallelized …

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  • xLearn - High-Performance and Scalable

    xLearn is a high performance, easy-to-use, and scalable machine learning package, which can be used to solve large-scale classification and regression problems. If you are the user of liblinear, libfm, or libffm, now the xLearn is your another better choice.

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  • useR! Machine Learning Tutorial - GitHub Pages

    2015-6-30 · This paper presents a complete approach to a successful utilization of a high-performance extreme learning machines (ELMs) Toolbox for Big Data. It summarizes recent advantages in algorithmic performance; gives a fresh view on the ELM solution in relation to the traditional linear algebraic performance; and reaps the latest software and hardware performance …

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  • GitHub - liujisi/xlearn: High Performance, Easy-to

    The h2o package connects to the h2o open source machine learning environment which has scalable implementations of random forests, GBM, GLM (with elastic net regularization), and deep learning. The randomForestSRC package can use both OpenMP as well as MPI for random forest extensions suitable for survival analysis, competing …

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  • High-Performance Extreme Learning Machines: A

    2017-3-24 · (Soon Deprecated) Use LightGBM in R (first wrapper available in R for LightGBM) tuned for maximum I/O without using in-memory dataset moves (which is both a good and bad thing! - 10GB of data takes 4 mins of travel in a HDD) and use feature importance with smart and readable plots - I recommend using official LightGBM R Package …

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  • Top 20 Best R Machine Learning Packages To

    1 天前 · High-performance machine learning algorithms implemented in 'Rcpp' and 'RcppEigen', including non-negative factorization of sparse matrices, projection of linear factor models, and non-negative least squares.

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  • CRAN - Package RcppML

    2017-3-24 · Advanced High Performance Data Science Toolbox for R by Laurae - Laurae2/Laurae ... NA cleaning, rank deficiency checking, pretty printed machine learning performance statistics (R, R^2, MAE, MSE, RMSE, MAPE, AUC, Logloss, optimistic Kappa, optimistic F1 Score, optimistic MCC, optimistic TPR, optimistic TNR, optimistic FPR, …

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  • GitHub - Laurae2/Laurae: Advanced High

    This CRAN task view contains a list of packages, grouped by topic, that are useful for high-performance computing (HPC) with R. In this context, we are defining 'high-performance computing' rather loosely as just about anything related to pushing R a little further: using compiled code, parallel computing (in both explicit and implicit modes), working with …

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  • GitHub - eddelbuettel/ctv-hpc: CRAN Task View:

    2015-6-30 · This paper presents a complete approach to a successful utilization of a high-performance extreme learning machines (ELMs) Toolbox for Big Data. It summarizes recent advantages in algorithmic performance; gives a fresh view on the ELM solution in relation to the traditional linear algebraic performance; and reaps the latest software and hardware performance …

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  • High-Performance Extreme Learning Machines: A

    2020-4-2 · This includes Machine Learning Services which contains Microsoft R and Python packages for high-performance predictive analytics and machine learning. Machine Learning Services offers an embedded, predictive analytics and data science engine that can execute R/Python code within a SQL Server database.

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  • Tutorials on Advanced Stats and Machine

    Machine learning (ML) is a field of computer science that uses algorithms and techniques for automating solutions to complex problems that are hard to program using conventional programming methods. Owing to the chemical versatility of organic building blocks, a large number of organic semi-conductors have b 2021 Energy and …

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  • R developer's guide to Azure - R programming -

    Interpretable Machine Learning with Python. This is the code repository for Interpretable Machine Learning with Python, published by Packt.. Learn to build interpretable high-performance models with hands-on real-world examples

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  • Machine learning for high performance organic

    2021-7-23 · Using this toolkit, you can: Deliver high-performance deep learning (DL) training on Intel® XPUs and integrate fast inference into your AI development workflow with Intel-optimized DL frameworks: TensorFlow* and PyTorch*, pretrained models, and low-precision tools. Achieve drop-in acceleration for data preprocessing and machine learning …

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  • Top 20 Best R Machine Learning Packages To

    1 天前 · High-performance machine learning algorithms implemented in 'Rcpp' and 'RcppEigen', including non-negative factorization of sparse matrices, projection of linear factor models, and non-negative least squares.

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  • CRAN - Package RcppML

    2018-2-16 · Microsoft R Client is a free, data science tool for high-performance analytics that you can install on Windows client operating systems. R Client is built on top of Microsoft R Open so you can use any open-source R packages to build your analytics, and includes the R function libraries from Microsoft that execute locally on R Client or remotely on a more powerful Machine Learning …

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  • A Tool Kit for Working with Time Series in R •

    2020-6-3 · Machine Learning with R and H2O by Mark Landry with assistance from Spencer Aiello, Eric Eckstrand, Anqi Fu, & Patrick Aboyoun Edited by: Angela Bartz

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  • Install Microsoft R Client on Windows | Microsoft

    2021-7-22 · This summer I’ve been competing in the SLICED machine learning competition, where contestants have two hours to open a new dataset, build a predictive model, and be scored as a Kaggle submission. Contestants are graded primarily on model performance, but also get points for visualization and storytelling, and from audience …

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  • Mark Landry Edited by: Angela Bartz - H2O.ai

    2018-8-30 · Algorithm Intermediate Machine Learning Python R Structured Data Supervised Technique Time Series Time Series Forecasting Build High Performance Time Series Models using Auto ARIMA in Python and R Aishwarya Singh , August 30, 2018

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  • CRAN - Package RcppML

    1 天前 · High-performance machine learning algorithms implemented in 'Rcpp' and 'RcppEigen', including non-negative factorization of sparse matrices, projection of linear factor models, and non-negative least squares.

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  • CRAN Task View: High-Performance and Parallel

    2021-5-27 · This CRAN task view contains a list of packages, grouped by topic, that are useful for high-performance computing (HPC) with R. In this context, we are defining 'high-performance computing' rather loosely as just about anything related to pushing R a little further: using compiled code, parallel computing (in both explicit and implicit modes), …

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  • fastJT: An R package for robust and efficient

    fastJT is an open-source R extension package, applying the Jonckheere-Terpstra statistic for robust feature selection for machine learning and association studies. The package implements an efficient algorithm which leverages internal information among the samples to avoid unnecessary computations, …

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  • Rcpp · Advanced R.

    2019-8-8 · High performance functions with Rcpp. ... ('Rcpp-package'). Learning more. This chapter has only touched on a small part of Rcpp, giving you the basic tools to rewrite poorly performing R code in C++. The Rcpp book is the best reference to learn more about Rcpp. As noted, Rcpp has many other capabilities that make it easy to interface R …

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  • Your First Machine Learning Project in R Step-By

    Machine learning (ML) is a field of computer science that uses algorithms and techniques for automating solutions to complex problems that are hard to program using conventional programming methods. Owing to the chemical versatility of organic building blocks, a large number of organic semi-conductors have b 2021 Energy and …

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  • Machine learning for high performance organic

    2020-11-23 · The R package mlr (Machine Learning in R) 33 was used as a framework to carry out benchmarking and all code for the experiments was written in R 34. The visualisations were created in Python. The ...

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  • A comparison of machine learning methods for

    2019-10-15 · Gradient Boosting Machine is a powerful machine-learning technique that has shown considerable success in a wide range of practical applications . In this research study, we used Logistic Regression and Gradient Boosting Machine techniques in the MLR package in R to build predictive models.

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  • Chapter 11 Random Forests | Hands-On Machine

    2020-9-8 · There exist high-level libraries which use some of these previous mentioned libraries as their base in order to make machine learning easier for the data scientist. huggingface.co One of the top machine learning libraries is huggingface.co’s , which creates good base models for researchers built on top of TensorFlow and PyTorch.

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  • CRAN - Package RcppML

    1 天前 · High-performance machine learning algorithms implemented in 'Rcpp' and 'RcppEigen', including non-negative factorization of sparse matrices, projection of linear factor models, and non-negative least squares.

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  • Performance · Advanced R.

    2019-8-8 · For really high-performance code, you can move outside of R and use another programming language. Rcpp will teach you the absolute minimum you need to know about C++ so you can write fast code using the Rcpp package. To really understand the performance of built-in base functions, you’ll need to learn a little bit about R’s C API.

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  • Machine learning for high performance organic

    Machine learning (ML) is a field of computer science that uses algorithms and techniques for automating solutions to complex problems that are hard to program using conventional programming methods. Owing to the chemical versatility of organic building blocks, a large number of organic semi-conductors have b 2021 Energy and …

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  • Python Vs R: What’s Best for Machine Learning |

    2019-4-9 · In this machine learning use case I will show how to use SAP HANAs machine learning library – Predictive Analysis Library to solve this data science challenge. The data is available so if you want to re-produce it should be straight forward. Building and training machine learning models from R – but on SAP HANA.

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  • Machine Learning with SAP HANA – from R |

    2016-3-28 · Introduction. We have several machine learning algorithms at our disposal for model building. Doing data based prediction is now easier like never before. Whether it is a regression or classification problem, one can effortlessly achieve a reasonably high accuracy using a suitable algorithm.

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  • Imbalanced Classification Problems in R

    2020-6-29 · treeheatr: an R package for interpretable decision tree visualizations. This manuscript () was automatically generated from trang1618/[email protected] on June 29, 2020.Authors. Trang T. Le 0000-0003-3737-6565 · trang1618 · trang1618 Department of Biostatistics, Epidemiology and Informatics, …

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  • treeheatr: an R package for interpretable decision

    2017-5-1 · Autoencoders and anomaly detection with machine learning in fraud analytics. Tweet. 01 May 2017. All my previous posts on machine learning have dealt with supervised learning. But we can also use machine learning for unsupervised learning. The latter are e.g. used for clustering and (non-linear) dimensionality reduction.

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  • Autoencoders and anomaly detection with

    Welcome to the UC Irvine Machine Learning Repository! We currently maintain 588 data sets as a service to the machine learning community. You may view all data sets through our searchable interface. For a general overview of the Repository, please visit our About page.For information about citing data sets in publications, please read our citation …

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