An NLP library for building bots, with entity extraction, sentiment analysis, automatic language identify, and so more

A collection of research papers on decision, classification and regression trees with implementations.

A Python interface for Facebook fastText

This repository provides code for machine learning algorithms for edge devices developed at Microsoft Research India.

A curated list of data mining papers about fraud detection.

A curated list of gradient boosting research papers with implementations.

Repository for the paper "Automated Hate Speech Detection and the Problem of Offensive Language", ICWSM 2017

A Naive Bayes machine learning implementation in Elixir.

Natural language detection library for Rust. Try demo online: https://www.greyblake.com/whatlang/

custom human activity recognition modules by pose estimation and cascaded inference using sklearn API

Organize your folders into a beautiful classified folder structure with this perfect tool

A java classifier based on the naive Bayes approach complete with Maven support and a runnable example.

This is a pytorch re-implementation of Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition

A pytorch implemented classifier for Multiple-Label classification

ERRor ANnotation Toolkit: Automatically extract and classify grammatical errors in parallel original and corrected sentences.

An example of how to use CreateML in Xcode 10 to create a Core ML model for classifying text

A License Classifier

ALBERT model Pretraining and Fine Tuning using TF2.0

yet another general purpose naive bayesian classifier.

Scene text detection and recognition based on Extremal Region(ER)

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

Machine Learning inference engine for Microcontrollers and Embedded devices

A Machine Learning classifier for recognizing the digits for humans.

📊 Naive Bayes classifier for JavaScript

Object classification with CIFAR-10 using transfer learning

This case study shows how to create a model for text analysis and classification and deploy it as a web service in Azure cloud in order to automatically classify support tickets. This project is a proof of concept made by Microsoft (Commercial Software Engineering team) in collaboration with Endava http://endava.com/en

The purpose of this tutorial is to learn how to install and prepare TensorFlow framework to train your own convolutional neural network object detection classifier for multiple objects, starting from scratch

Efficient tree-based convolutional neural networks in TensorFlow

A sophisticated smart symptom search engine

🐒 R package for text analysis with Monkeylearn 🐒