282 Open source projects that are alternatives of or similar to Programming Language Classifier

Create ML playgrounds for building machine learning models. For developers and data scientists.

A Machine Learning classifier for recognizing the digits for humans.

YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

Code examples for ARKit.

Natural language detection package in pure Go

English (natural language) parser

A NSFW (aka porn) detector with CoreML

Adversarial Training for Natural Language Understanding

Python implementation of TextRank for phrase extraction and summarization of text documents

A simple demo for Core ML

OpenPose Example App

A sophisticated smart symptom search engine

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

☄️ Node.js library to access IBM Watson services.

Almost Real-time Object Detection using Apple's CoreML and YOLO v1 -

📚Curated list of articles, web-resources, tutorials and code repositories that may help you dig a little bit deeper into iOS [and Apple Platforms].

🤖 The Cocoa Artificial Intelligence Lab

⛔️ ARCHIVED ⛔️ 🐒 R package for text analysis with Monkeylearn 🐒

A Show And Tell implementation for iOS 11.0 based on CoreML

Face Landmark Detector based on Mobilenet V1

A CoreML model which classifies images of food

This native plugin enables Unity to take advantage of specific features of Core-ML and Vision Framework on the iOS platform.

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

Simple project to detect objects and display 3D labels above them in AR. This serves as a basic Template for an ARKit project to use CoreML.

simple rules engine

Source code for my blog post series "On-device training with Core ML"

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

Discover, download, compile & launch different image processing & style transfer CoreML models on iOS.

Automatically interpret and validate nested natural logic arguments based on rules of inference and propositional logic

Machine Learning inference engine for Microcontrollers and Embedded devices

This is a sample project demonstrating the use of Keras (Tensorflow) for the training of a MNIST model for handwriting recognition using CoreML on iOS 11 for inference.

Using Machine Learning to predict the type of exercise from movement data

yet another general purpose naive bayesian classifier.

😃 iOS11 demo application for sentiment polarity analysis.

Real-time object-detection using SSD on Mobilenet on iOS using CoreML, exported using tf-coreml

TypeScript framework to build cross-platform voice applications (alexa, google home, ...).

A curated list of pretrained sentence and word embedding models

An official PyTorch implementation of the paper "Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language", NeurIPS 2018

📊 Naive Bayes classifier for JavaScript

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

convert facenet and mtcnn models from tensorflow to tensorflow lite and coreml （使用 TFLite 将 FaceNet 和 MTCNN 移植到移动端）

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.

Object classification with CIFAR-10 using transfer learning

Forte is a flexible and powerful NLP builder FOR TExt. This is part of the CASL project: http://casl-project.ai/

Create regex from natural language

🌸 iOS11 demo application for flower classification.

plugin to check for possible insensitive, inconsiderate language

Fast Porter stemmer implementation

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].

Building classifiers using cancer transcriptomes across 33 different cancer-types

Natural Language Concrete Syntax Tree format

plugin to check phrases for simpler alternatives

Hand-gesture recognition on iOS app using CoreML

Real-time Number Recognition using Apple's CoreML 2.0 and MNIST -

Node.js client for Google Cloud Natural Language: Derive insights from unstructured text using Google machine learning.

Source code for the book Core ML Survival Guide

The pluggable natural language linter for text and markdown.

I checked the performance by running PoseNet on CoreML