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500lines

500 Lines or Less

bigdata-interview

:dart: :star2:[大数据面试题]分享自己在网络上收集的大数据相关的面试题以及自己的答案总结.目前包含Hadoop/Hive/Spark/Flink/Hbase/Kafka/Zookeeper框架的面试题知识总结

bigdata_analyse

大数据分析项目

csi-gesture-recognition-master

csi_reader

d2l-zh-pytorch-slides

Pytorch版代码幻灯片

dl-on-flink

echarts

Apache ECharts is a powerful, interactive charting and data visualization library for browser

flink-cdc-connectors

Change Data Capture (CDC) Connectors for Apache Flink

god-of-bigdata

专注大数据学习面试，大数据成神之路开启。Flink/Spark/Hadoop/Hbase/Hive...

how-to-make-a-computer-operating-system

How to Make a Computer Operating System in C++

java-eight-part

『Java八股文』Java面试套路，Java进阶学习，打破内卷拿大厂Offer，升职加薪！

linux-80211n-csitool-supplementary

802.11n CSI Tool based on iwlwifi and Linux-2.6

mapreduce-mutli

mtad-gat-pytorch

PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).

mydb

一个简单的数据库实现

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].

test

项目描述