codeur66,SL,github

scikits.timeseries-sandbox

A library to handle time series in NumPy/SciPy

shap

A game theoretic approach to explain the output of any machine learning model.

sklearn-docbuilder

Script to configure a cloud server to build the documentation and plots and update the sklearn website

sklearn_pycon2014

Repository containing files for my PyCon 2014 scikit-learn tutorial.

slot_filling_and_intent_detection_of_slu

slot filling, intent detection, joint training, ATIS & SNIPS datasets, MIT corpus, ELMo, BERT, XLNet

sparse

Sparse multi-dimensional arrays for the PyData ecosystem

speech_signal_processing_and_classification

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].

statistical-learning-method

《统计学习方法》笔记-基于Python算法实现

symposion

PyData's fork of Pinax's symposion. See https://github.com/pydata/conf_site/.

tensorboard

TensorFlow's Visualization Toolkit

tensorflow

An Open Source Machine Learning Framework for Everyone

tensorrt

TensorRT is a C++ library for high performance inference on NVIDIA GPUs and deep learning accelerators.

tensorrt-1

TensorFlow/TensorRT integration

terraform-flask

A CI/CD production ready pipeline for a python flask application using the DevOps tools: git for code repository, docker and docker hub for building and storing the flask application, Jenkins pipeline for continuous integration, ansible for infrastructure provisioning and Kubernetes to launch flask app in a cluster, ELK stack -monitoring.