Practical Machine Learning With PythonMaster the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.
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Python nlp tutorialThis repository provides everything to get started with Python for Text Mining / Natural Language Processing (NLP)
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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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Nltk Book ResourceNotes and solutions to complement the official NLTK book
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TextblobSimple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
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NltkNLTK Source
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Text Analytics With PythonLearn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.
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PycantoneseCantonese Linguistics and NLP in Python
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PynlplPyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. There are also more complex data types and algorithms. Moreover, there are parsers for file formats common in NLP (e.g. FoLiA/Giza/Moses/ARPA/Timbl/CQL). There are also clients to interface with various NLP specific servers. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotation).
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Gpt 2 PytorchSimple Text-Generator with OpenAI gpt-2 Pytorch Implementation
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Ner LstmNamed Entity Recognition using multilayered bidirectional LSTM
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Conv EmotionThis repo contains implementation of different architectures for emotion recognition in conversations.
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StanzaOfficial Stanford NLP Python Library for Many Human Languages
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TalismanStraightforward fuzzy matching, information retrieval and NLP building blocks for JavaScript.
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Deep Semantic Similarity ModelMy Keras implementation of the Deep Semantic Similarity Model (DSSM)/Convolutional Latent Semantic Model (CLSM) described here: http://research.microsoft.com/pubs/226585/cikm2014_cdssm_final.pdf.
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Cdqa⛔ [NOT MAINTAINED] An End-To-End Closed Domain Question Answering System.
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SentencepieceUnsupervised text tokenizer for Neural Network-based text generation.
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FewrelA Large-Scale Few-Shot Relation Extraction Dataset
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Paper ReadingPaper reading list in natural language processing, including dialogue systems and text generation related topics.
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Nlp RecipesNatural Language Processing Best Practices & Examples
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SeqevalA Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
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PythainlpThai Natural Language Processing in Python.
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SeqganA simplified PyTorch implementation of "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient." (Yu, Lantao, et al.)
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Fast abs rlCode for ACL 2018 paper: "Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting. Chen and Bansal"
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Ml paper notes📖 Notes and summaries of some Machine Learning / Computer Vision / NLP papers.
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Me botBuild a bot that speaks like you!
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DoccanoOpen source annotation tool for machine learning practitioners.
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Neural Vqa❔ Visual Question Answering in Torch
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RnnlgRNNLG is an open source benchmark toolkit for Natural Language Generation (NLG) in spoken dialogue system application domains. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.
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Ml MiptOpen Machine Learning course at MIPT
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Awesome Relation Extraction📖 A curated list of awesome resources dedicated to Relation Extraction, one of the most important tasks in Natural Language Processing (NLP).
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Deep Learning GuideAn evolving guide to learning Deep Learning effectively.
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Learn Data Science For FreeThis repositary is a combination of different resources lying scattered all over the internet. The reason for making such an repositary is to combine all the valuable resources in a sequential manner, so that it helps every beginners who are in a search of free and structured learning resource for Data Science. For Constant Updates Follow me in …
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StealthAn open source Ruby framework for text and voice chatbots. 🤖
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