1118 Open source projects that are alternatives of or similar to Mishkal

A curated list of awesome projects and dev/design resources for supporting Arabic computational needs.

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Simple setup for a React-Django web app.

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This repo contains my coursework, assignments, and Slides for Natural Language Processing Specialization by deeplearning.ai on Coursera

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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].

阿里天池首届中文NL2SQL挑战赛top6

This repository contains landmark research papers in Natural Language Processing that came out in this century.

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Cantonese Linguistics and NLP in Python

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📖 A curated list of resources dedicated to Natural Language Processing (NLP)

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Japanese negative positive classification.日本語文書のネガポジを判定。

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This repository is for my students of Udemy. You can find all lecture codes along with mentioned files for reading in here. So, feel free to clone it and if you have any problem just raise a question.

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互联网大厂面试经验

SLING - A natural language frame semantics parser

Worth-reading papers and related awesome resources on aspect-based sentiment analysis (ABSA). 值得一读的方面级情感分析论文与相关资源集合

The CRF Layer was implemented by using Chainer 2.0. Please see more details here: https://createmomo.github.io/2017/09/12/CRF_Layer_on_the_Top_of_BiLSTM_1/

Official Python client for the MonkeyLearn API. Build and consume machine learning models for language processing from your Python apps.

How to build RNNs and LSTMs from scratch with NumPy.

PostCSS plugin for RTL-adaptivity

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Example Play Slick Project