569 Open source projects that are alternatives of or similar to createml-playgrounds

An experimental CreateML project for predicting playing musical key and scale in realtime

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

Hand-Drawn Emoji Classifier (WWDC18 Scholarship Application)

Largest list of models for Core ML (for iOS 11+)

An open-source CoreML model trained on the ESC10 dataset

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Simple convolutional neural network to predict handwritten digits using Keras + CoreML for WWDC '18 scholarship [Accepted]

Visual programming language written in Swift that assembles to executable Swift code. WWDC '18 scholarship submission.

simple rules engine

licensechecker (lc) a command line application which scans directories and identifies what software license things are under producing reports as either SPDX, CSV, JSON, XLSX or CLI Tabular output. Dual-licensed under MIT or the UNLICENSE.

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

The challenge projects for Inferencing machine learning models on iOS

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ALBERT model Pretraining and Fine Tuning using TF2.0

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Using modanet fashion dataset, the clothes images were classified under 5 season (summer,winter,spring,autumn,all).

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

Machine learning to classify Malicious (Spam)/Benign URL's

A pytorch implemented classifier for Multiple-Label classification

Building classifiers using cancer transcriptomes across 33 different cancer-types

A Swift playground to automatically generate personalized conference badges.

A simple web app that shows how Watson's Natural Language Classifier (NLC) can classify ICD-10 code. The app is written in Python using the Flask framework and leverages the Watson Developer Cloud Python SDK

A License Classifier

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

Build your iOS 11+ apps with the ready-to-use Core ML models below

Machine learning based text classification in JavaScript using n-grams and cosine similarity

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

Nodejs binding for fasttext representation and classification.

A curated list of data mining papers about fraud detection.

sdkmesh decoder

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

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].

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

run a multitude of classifiers on you data and get an AUC report

A Naive Bayes machine learning implementation in Elixir.

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

Predict handwritten digits with CoreML

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Search the best feature subset for you classification mode

Menthor Editor

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A Machine Learning classifier for recognizing the digits for humans.

The goal of this project was to develop a Face Recognition application using a Local Binary Pattern approach and, using the same approach, develop a real time Face Recognition application.

Classify short texts with neural network.

Object classification with CIFAR-10 using transfer learning

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Simple image classifier microservice using tensorflow and sanic

A playground to gain a wider and deeper knowledge of the libraries in the Kotlin ecosystem. Also the official sample for gradle refreshVersions.

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