This is a repo with links to everything you'd ever want to learn about data engineering
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TranslatedAiLearning: Data Analysis + Machine Learning in Action + Linear Algebra + PyTorch + NLTK + TF2
- fp-growth
- apriori
- mahchine-leaning
- naivebayes
✂A collection of useful .htaccess snippets.
- apache
- dev-ops
- awesome
- awesome-list
TranslatedApacheCN collection of translated texts on data structures and algorithms
Apache HTTP server boilerplate configs
- apache
- httpd
- httpd-configuration
- config
Practical Gremlin - An Apache TinkerPop Tutorial
- graph-database
- gremlin
- tinkerpop
- gremlin-console
A collection of resources covering different aspects of JMeter usage.
- awesome
- jmeter
- jmeter-plugin
- load-testing
🔐 Tutorial of setting up Security for your API with one way authentication with TLS/SSL and mutual authentication for a java based web server and a client with both Spring Boot. Different clients are provided such as Apache HttpClient, OkHttp, Spring RestTemplate, Spring WebFlux WebClient Jetty and Netty, the old and the new JDK HttpClient, the old and the new Jersey Client, Google HttpClient, Unirest, Retrofit, Feign, Methanol, vertx, Scala client Finagle, Featherbed, Dispatch Reboot, AsyncHttpClient, Sttp, Akka, Requests Scala, Http4s Blaze, Kotlin client Fuel, http4k, Kohttp and ktor. Also other server examples are available such as jersey with grizzly. Also gRPC, WebSocket and ElasticSearch examples are included
- tls
- truststore
- certificate
- keystore
The code repository for the prototypes included in the eBook "Inspired EHRs - Designing for Clinicians" (inspiredEHRs.gov). The code of the prototypes is made available under the Apache 2.0 open source license. This license agreement allows anyone to freely use the code and ideas presented in this book, subject to the conditions listed at http://opensource.org/licenses/Apache-2.0.
- healthcare
- ehr
- open-source
- electronic-health-record
Translated302API developer materials and integration examples: Python / Node.js / cURL, client configuration, frontend components, and a local bill verification calculator. Open source under Apache-2.0; please keep the NOTICE attribution when redistributing.
Big Data essentials: Hadoop, MapReduce, Spark. Explore tutorials and demos in Jupyter notebooks—most are self-contained and live, ready to run with a click.
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
- svm
- regression
- classification
- machine-learning