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limdu - Machine-learning for Node.js
Limdu is a machine-learning framework for Node.js. It supports multi-label classification, online learning, and real-time classification. Therefore, it is especially suited for natural language understanding in dialog systems and chat-bots.Limdu is in an "alpha" state - some parts are working (see this readme), but some parts are missing or not tested. Contributions are welcome.
classifier classification categorization text-classification natural-lanaguage-understanding machine-learning multi-label multilabel multi-class multiclass online-learning naive-bayes winnow perceptron svm linear-svm binary-relevance one-vs-allfastText - Library for fast text representation and classification.
fastText is a library for efficient learning of word representations and sentence classification. You can find answers to frequently asked questions on our website.
text-classification text-representation classificationApache Mahout - Scalable machine learning library
Apache Mahout has implementations of a wide range of machine learning and data mining algorithms: clustering, classification, collaborative filtering and frequent pattern mining.
machine-learning classification data-mining fuzzyTensorFlow-Book - Accompanying source code for Machine Learning with TensorFlow
This is the official code repository for Machine Learning with TensorFlow. Get started with machine learning using TensorFlow, Google's latest and greatest machine learning library.
tensorflow machine-learning regression convolutional-neural-networks logistic-regression book reinforcement-learning autoencoder linear-regression classification clusteringHyperGAN - A composable Generative Adversarial Network(GAN) with API and command line tool.
A composable GAN API and CLI. Built for developers, researchers, and artists. HyperGAN is currently in open beta.
gan supervised-learning unsupervised-learning learning generative-adversarial-network generative-model artificial-intelligence machine-learning machine-learning-api tensorflow classification generator discriminatorOryx 2 - Lambda architecture on Apache Spark, Apache Kafka for real-time large scale machine learning
The Oryx open source project provides infrastructure for lambda-architecture applications on top of Spark, Spark Streaming and Kafka. On this, it provides further support for real-time, large scale machine learning, and end-to-end applications of this support for common machine learning use cases, like recommendations, clustering, classification and regression.
lambda lambda-architecture oryx apache-spark machine-learning kafka classification clusteringScikit Learn - Machine Learning in Python
scikit-learn is a Python module for machine learning built on top of SciPy. It is simple and efficient tools for data mining and data analysis. It supports automatic classification, clustering, model selection, pre processing and lot more.
machine-learning data-mining data-analysis classificationtext_classification - all kinds of text classificaiton models and more with deep learning
the purpose of this repository is to explore text classification methods in NLP with deep learning. sentence similarity project has been released you can check it if you like.
classification nlp fasttext textcnn textrnn tensorflow multi-label multi-class attention-mechanism text-classification convolutional-neural-networks sentence-classification memory-networksmlr - mlr: Machine Learning in R
Please cite our JMLR paper [bibtex]. Some parts of the package were created as part of other publications. If you use these parts, please cite the relevant work appropriately. An overview of all mlr related publications can be found here.
machine-learning data-science tuning cran r-package predictive-modeling classification regression statistics r survival-analysis imbalance-correction tutorial mlr learners hyperparameters-optimization feature-selection multilabel-classification clustering stackingtensorflow_cookbook - Code for Tensorflow Machine Learning Cookbook
This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.
tensorflow tensorflow-cookbook linear-regression neural-network tensorflow-algorithms rnn cnn svm nlp packtpub machine-learning tensorboard classification regression kmeans-clustering genetic-algorithm odeawesome-project-ideas - Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas
Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas
deep-learning forecasting machine-learning classification series-forecasting image-classification awesome-list awesome dataset multi-label-classificationphp-ml - PHP-ML - Machine Learning library for PHP
Fresh approach to Machine Learning in PHP. Algorithms, Cross Validation, Neural Network, Preprocessing, Feature Extraction and much more in one library. PHP-ML requires PHP >= 7.1.
machine-learning classification cross-validation feature-extraction artificial-intelligence neural-network data-sciencepointnet - PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Created by Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas from Stanford University. This work is based on our arXiv tech report, which is going to appear in CVPR 2017. We proposed a novel deep net architecture for point clouds (as unordered point sets). You can also check our project webpage for a deeper introduction.
point-cloud classification segmentation neural-network tensorflow geometry-processingPyTorch-Tutorial - Build your neural network easy and fast
In these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. Thanks for liufuyang's notebook files which is a great contribution to this tutorial.
neural-network pytorch-tutorial batch-normalization cnn rnn autoencoder pytorch regression classification batch tutorial dropout dqn reinforcement-learning gan generative-adversarial-network machine-learningTensorflow-Tutorial - Tensorflow tutorial from basic to hard
In these tutorials, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. All methods mentioned below have their video and text tutorial in Chinese. Visit 莫烦 Python for more.
tensorflow tensorflow-tutorials gan generative-adversarial-network rnn cnn classification regression autoencoder deep-q-network dqn machine-learning tutorial dropout neural-networkpractical-machine-learning-with-python - Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system
"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-networkslabelme - Image Polygonal Annotation with Python (polygon, rectangle, line, point and image-level flag annotation)
Labelme is a graphical image annotation tool inspired by http://labelme.csail.mit.edu. It is written in Python and uses Qt for its graphical interface. Fig 2. VOC dataset example of instance segmentation.
image-annotation computer-vision annotations deep-learning semantic-segmentation instance-segmentation video-annotation classificationMLIB - Apache Spark's scalable machine learning library
MLlib is a Spark implementation of some common machine learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction and lot more.
machine-learning data-mining data-analysis classificationpointnet2 - PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Created by Charles R. Qi, Li (Eric) Yi, Hao Su, Leonidas J. Guibas from Stanford University. This work is based on our NIPS'17 paper. You can find arXiv version of the paper here or check project webpage for a quick overview. PointNet++ is a follow-up project that builds on and extends PointNet. It is version 2.0 of the PointNet architecture.
point-cloud deep-learning classification segmentation 3d-shapePythonMachineLearning - Practice and tutorial-style notebooks covering wide variety of machine learning techniques
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-bayes
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