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chattoChatto is a minimal chatbot framework in Go.
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dl-reluDeep Learning using Rectified Linear Units (ReLU)
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Awesome Decision Tree PapersA collection of research papers on decision, classification and regression trees with implementations.
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opfython🌳 A Python-inspired implementation of the Optimum-Path Forest classifier.
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Lclicensechecker (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.
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golinearliblinear bindings for Go
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labelReaderProgrammatically find and read labels using Machine Learning
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EmlearnMachine Learning inference engine for Microcontrollers and Embedded devices
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tbcnnEfficient tree-based convolutional neural networks in TensorFlow
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Speech signal processing and classificationFront-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].
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VALISVote ALlocating Immune System, an immune-inspired classification algorithm
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smalltextClassify short texts with neural network.
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dhtbayA DHT crawler and torrent indexer
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node-fasttextNodejs binding for fasttext representation and classification.
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