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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.

https://github.com/erelsgl/limdu Dependencies:

brain : *

graph-paths : latest

languagemodel : latest

sprintf : *

svm : *

temp : *

underscore : *

wordsworth : *

async : *

Python codes for common Machine Learning Algorithms

linear-regression polynomial-regression logistic-regression decision-trees random-forest svm svr knn-classification naive-bayes-classifier kmeans-clustering hierarchical-clustering pca lda xgboost-algorithmPyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers. threshold is added in version 0.9 for real value prediction.

machine-learning confusion-matrix matrix statistics statistical-analysis accuracy ml ai mathematics data-mining data-analysis classification classifier data-science data neural-network multiclass-classification deep-learning artificial-intelligence deeplearningThe mission of ThunderSVM is to help users easily and efficiently apply SVMs to solve problems. ThunderSVM exploits GPUs and multi-core CPUs to achieve high efficiency. It supports all functionalities of LibSVM such as one-class SVMs, SVC, SVR and probabilistic SVMs. It can use same command line options as LibSVM. It supports Python, R and Matlab interfaces.

svm classification regression one-class-learning parallelism cuda support-vector-machineThis package provides a Ruby bindings to the LIBSVM library. SVM is a machine learning and classification algorithm, and LIBSVM is a popular free implementation of it, written by Chih-Chung Chang and Chih-Jen Lin, of National Taiwan University, Taipei. See the book "Programming Collective Intelligence," among others, for a usage example. There is a JRuby implementation of this gem named jrb-libsvm by Andreas Eger.

libsvm ruby-bindings ruby-language-bindings machine-learning svm svm-classifier svm-training svm-learning ml rubymlWe use and compare various different methods for sentiment analysis on tweets (a binary classification problem). The training dataset is expected to be a csv file of type tweet_id,sentiment,tweet where the tweet_id is a unique integer identifying the tweet, sentiment is either 1 (positive) or 0 (negative), and tweet is the tweet enclosed in "". Similarly, the test dataset is a csv file of type tweet_id,tweet. Please note that csv headers are not expected and should be removed from the training and test datasets. There are some general library requirements for the project and some which are specific to individual methods. The general requirements are as follows.

machine-learning deeplearning sentiment-analysis sentiment-classification cnn keras lstmThis 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 odethe 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-networksNeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios. A salient feature is that NeuralClassifier currently provides a variety of text encoders, such as FastText, TextCNN, TextRNN, RCNN, VDCNN, DPCNN, DRNN, AttentiveConvNet and Transformer encoder, etc. It also supports other text classification scenarios, including binary-class and multi-class classification. It is built on PyTorch. Experiments show that models built in our toolkit achieve comparable performance with reported results in the literature. Detail configurations and explanations see Configuration.

The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem. Existing solutions usually involve class-balancing strategies, e.g., by loss re-weighting, data re-sampling, or transfer learning from head- to tail-classes, but all of them adhere to the scheme of jointly learning representations and classifiers. In this work, we decouple the learning procedure into representation learning and classification, and systematically explore how different balancing strategies affect them for long-tailed recognition. The findings are surprising: (1) data imbalance might not be an issue in learning high-quality representations; (2) with representations learned with the simplest instance-balanced (natural) sampling, it is also possible to achieve strong long-tailed recognition ability with relative ease by adjusting only the classifier. We conduct extensive experiments and set new state-of-the-art performance on common long-tailed benchmarks like ImageNet-LT, Places-LT and iNaturalist, showing that it is possible to outperform carefully designed losses, sampling strategies, even complex modules with memory, by using a straightforward approach that decouples representation and classification. The code is based on https://github.com/zhmiao/OpenLongTailRecognition-OLTR.

The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution. These tasks are usually required to build more advanced text processing services. OpenNLP also includes maximum entropy and perceptron based machine learning.

nlp natural-language-processing machine-learning parts-of-speech categorization auto-tagging sentence-detectorA Naive Bayes machine learning implementation in Elixir. In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.

naive-bayes-classifier bayes machine-learning classifierTensorflow implementation of model discussed in the following paper: Learning to Diagnose with LSTM Recurrent Neural Networks. MIMIC-III dataset can possibly be use to train and test the model. Beware this is not the data set used by the authors of the paper.

deep-learning time-series tensorflow lstm multi-label-classificationMachine Learning Library for .NET. Initial inclusions will be binary and multi-class classification as well as standard clustering algorithms.

ai artificial-intellige information-theory linear-algebra machine-learningCurated 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-classificationThis 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-networksSupport Vector Machine (SVM) library for nodejs & io.js . Support vector machines are supervised learning models that analyze data and recognize patterns. A special property is that they simultaneously minimize the empirical classification error and maximize the geometric margin; hence they are also known as maximum margin classifiers.

svm machine-learning libsvmThe Neural Monkey package provides a higher level abstraction for sequential neural network models, most prominently in Natural Language Processing (NLP). It is built on TensorFlow. It can be used for fast prototyping of sequential models in NLP which can be used e.g. for neural machine translation or sentence classification. The higher-level API brings together a collection of standard building blocks (RNN encoder and decoder, multi-layer perceptron) and a simple way of adding new building blocks implemented directly in TensorFlow.

neural-machine-translation tensorflow nlp sequence-to-sequence neural-networks nmt machine-translation mt deep-learning image-captioning encoder-decoder gpuConjecture is a framework for building machine learning models in Hadoop using the Scalding DSL. The goal of this project is to enable the development of statistical models as viable components in a wide range of product settings. Applications include classification and categorization, recommender systems, ranking, filtering, and regression (predicting real-valued numbers). Conjecture has been designed with a primary emphasis on flexibility and can handle a wide variety of inputs. Integration with Hadoop and scalding enable seamless handling of extremely large data volumes, and integration with established ETL processes. Predicted labels can either be consumed directly by the web stack using the dataset loader, or models can be deployed and consumed by live web code. Currently, binary classification (assigning one of two possible labels to input data points) is the most mature component of the Conjecture package.There are a few stages involved in training a machine learning model using Conjecture.

I 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-competitionRumale (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-intelligence
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