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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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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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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TextblobSimple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
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Python Tutorial NotebooksPython tutorials as Jupyter Notebooks for NLP, ML, AI
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StocksightStock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis
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Nltk dataNLTK Data
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Nltk Book ResourceNotes and solutions to complement the official NLTK book
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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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ClustypeAutomatic Entity Recognition and Typing for Domain-Specific Corpora (KDD'15)
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PytorchnlpbookCode and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://nlproc.info
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Papers读过的CV方向的一些论文，图像生成文字、弱监督分割等
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D2l EnInteractive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge.
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AnagoBidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.
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Awesome Machine Learning📖 List of some awesome university courses for Machine Learning! Feel free to contribute!
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Ios mlList of Machine Learning, AI, NLP solutions for iOS. The most recent version of this article can be found on my blog.
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Chinese nlu by using rasa nlu使用 RASA NLU 来构建中文自然语言理解系统（NLU）| Use RASA NLU to build a Chinese Natural Language Understanding System (NLU)
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NeuronblocksNLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego
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ChatbotРусскоязычный чатбот
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Textaugmentation Gpt2Fine-tuned pre-trained GPT2 for custom topic specific text generation. Such system can be used for Text Augmentation.
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