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mlpack is an intuitive, fast, and flexible C++ machine learning library with bindings to other languages. It is meant to be a machine learning analog to LAPACK, and aims to implement a wide array of machine learning methods and functions as a "swiss army knife" for machine learning researchers. In addition to its powerful C++ interface, mlpack also provides command-line programs and Python bindings.

http://www.mlpack.org/https://github.com/mlpack/mlpack

Tags | machine-learning-library c-plus-plus deep-learning nearest-neighbor-search regression machine-learning |

Implementation | C++ |

License | BSD-3-Clause |

Platform | Windows Linux |

Jubatus is a distributed processing framework and streaming machine learning library. Jubatus includes these functionalities: Online Machine Learning Library: Classification, Regression, Recommendation (Nearest Neighbor Search), Graph Mining, Anomaly Detection, Clustering, Feature Vector Converter (fv_converter): Data Preprocess and Feature Extraction, Framework for Distributed Online Machine Learning with Fault Tolerance.

machine-learning machine-learning-framework distributedRumale (Ruby machine learning) is a machine learning library in Ruby. Rumale provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. Rumale supports Linear / Kernel Support Vector Machine, Logistic Regression, Linear Regression, Ridge, Lasso, Kernel Ridge, Factorization Machine, Naive Bayes, Decision Tree, AdaBoost, Gradient Tree Boosting, Random Forest, Extra-Trees, K-nearest neighbor classifier, K-Means, K-Medoids, Gaussian Mixture Model, DBSCAN, SNN, Power Iteration Clustering, Mutidimensional Scaling, t-SNE, Principal Component Analysis, Kernel PCA and Non-negative Matrix Factorization. This project was formerly known as "SVMKit". If you are using SVMKit, please install Rumale and replace SVMKit constants with Rumale.

machine-learning data-science data-analysis artificial-intelligenceThis repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations. After several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, not to provide the most efficient implementations.

machine-learning logistic-regression ipynb machine-learning-algorithms linear-regression perceptron python-implementations kmeans algorithm python3 neural-network k-nearest-neighbours k-nearest-neighbor k-nn neural-networksSmile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance.Smile covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc.

machine-learning nlp linear-algebra natural-language-processingDlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. See http://dlib.net for the main project documentation and API reference. Doing so will make some things run faster.

machine-learning deep-learning c-plus-plus computer-vision machine-learning-libraryDLL is a library that aims to provide a C++ implementation of Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) and their convolution versions as well. It also has support for some more standard neural networks. Note: When you clone the library, you need to clone the sub modules as well, using the --recursive option.

c-plus-plus cpp cpp11 cpp14 performance machine-learning deep-learning artificial-neural-networks gpu rbm cpu convolutional-neural-networksWould you like to build/train a model using Keras/Python? And would you like run the prediction (forward pass) on your model in C++ without linking your application against TensorFlow? Then frugally-deep is exactly for you. Layer types typically used in image recognition/generation are supported, making many popular model architectures possible (see Performance section).

tensorflow deep-learning keras cpp cpp14 header-only library c-plus-plus c-plus-plus-14 convolutional-neural-networks prediction machine-learningFor more detail, see the installation for instruction on how to build N2 from source. N2 is an approximate nearest neighborhoods algorithm library written in C++ (including Python/Go bindings). N2 provides a much faster search speed than other implementations when modeling large dataset. Also, N2 supports multi-core CPUs for index building.

ml knn machine-learning approximate k-nearest-neighbors nearest-neighbor-search approximate-nearest-neighbor-searchThe Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

machine-learning framework c-sharp nuget visual-studio statistics unity3d neural-network support-vector-machines computer-vision image-processing ffmpegThe Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition. Classification: Adaboost, Decision Tree, Dynamic Time Warping, Gaussian Mixture Models, Hidden Markov Models, k-nearest neighbor, Naive Bayes, Random Forests, Support Vector Machine, Softmax, and more...

gesture-recognition grt machine-learning gesture-recognition-toolkit support-vector-machine random-forest kmeans dynamic-time-warping softmax linear-regressiontiny-dnn is a C++14 implementation of deep learning. It is suitable for deep learning on limited computational resource, embedded systems and IoT devices. Check out the documentation for more info.

c-plus-plus deep-learning machine-learning neural-networkHLearn is a high performance machine learning library written in Haskell. For example, it currently has the fastest nearest neighbor implementation for arbitrary metric spaces (see this blog post). HLearn is also a research project. The research goal is to discover the "best possible" interface for machine learning. This involves two competing demands: The library should be as fast as low-level libraries written in C/C++/Fortran/Assembly; but it should be as flexible as libraries written in high level languages like Python/R/Matlab. Julia is making amazing progress in this direction, but HLearn is more ambitious. In particular, HLearn's goal is to be faster than the low level languages and more flexible than the high level languages.

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

numpy statistics pandas matplotlib regression scikit-learn classification principal-component-analysis clustering decision-trees random-forest dimensionality-reduction neural-network deep-learning artificial-intelligence data-science machine-learning k-nearest-neighbours naive-bayesI just built out v2 of this project that now gives you analytics info from your models, and is production-ready. machineJS is an amazing research project that clearly proved there's a hunger for automated machine learning. auto_ml tackles this exact same goal, but with more features, cleaner code, and the ability to be copy/pasted into production.

machine-learning data-science machine-learning-library machine-learning-algorithms ml data-scientists javascript-library scikit-learn kaggle numerai automated-machine-learning automl auto-ml neuralnet neural-network algorithms random-forest svm naive-bayes bagging optimization brainjs date-night sklearn ensemble data-formatting js xgboost scikit-neuralnetwork knn k-nearest-neighbors gridsearch gridsearchcv grid-search randomizedsearchcv preprocessing data-formatter kaggle-competitionThis is the code repository for TensorFlow Machine Learning Cookbook, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish. TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow.

Paracel is a distributed computational framework, designed for many machine learning problems: Logistic Regression, SVD, Matrix Factorization(BFGS, sgd, als, cg), LDA, Lasso... Firstly, paracel splits both massive dataset and massive parameter space. Unlike Mapreduce-Like Systems, paracel offers a simple communication model, allowing you to work with a global and distributed key-value storage, which is called parameter server.

machine-learning distributed-computing graph c-plus-plus"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

machine-learning deep-learning text-analytics classification clustering natural-language-processing computer-vision data-science spacy nltk scikit-learn prophet time-series-analysis convolutional-neural-networks tensorflow keras statsmodels pandas deep-neural-networksIntel MKL-DNN repository migrated to https://github.com/intel/mkl-dnn. The old address will continue to be available and will redirect to the new repo. Please update your links. Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for deep learning applications. The library accelerates deep learning applications and framework on Intel(R) architecture. Intel(R) MKL-DNN contains vectorized and threaded building blocks which you can use to implement deep neural networks (DNN) with C and C++ interfaces.

intel mkl-dnn deep-learning deep-neural-networks cnn rnn lstm c-plus-plus intel-architecture xeon xeon-phi atom core simd sse42 avx2 avx512 avx512-vnni performanceYtk-learn is a distributed machine learning library which implements most of popular machine learning algorithms

machine-learning distributed gbm gbdt logistic-regression factorization-machines spark hadoopTest tube is a python library to track and parallelize hyperparameter search for Deep Learning and ML experiments. It's framework agnostic and built on top of the python argparse API for ease of use. If you're a researcher, test-tube is highly encouraged as a way to post your paper's training logs to help add transparency and show others what you've tried that didn't work.

deep-learning machine-learning tensorflow hyperparameter-optimization neural-networks data-science keras pytorch caffe2 caffe chainer grid-search random-search
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