Download JAR files tagged by batch with all dependencies

The Finch HRIS API provides a unified way to connect to a multitide of HRIS systems. The API requires an access token issued by Finch. By default, Organization and Payroll requests use Finch's [Data Syncs](/development-guides/Data-Syncs). If a request is made before the initial sync has completed, Finch will request data live from the provider. The latency on live requests may range from seconds to minutes depending on the provider and batch size. For automated integrations, Deductions requests (both read and write) are always made live to the provider. Latencies may range from seconds to minutes depending on the provider and batch size. Employer products are specified by the product parameter, a space-separated list of products that your application requests from an employer authenticating through Finch Connect. Valid product names are— - `company`: Read basic company data - `directory`: Read company directory and organization structure - `individual`: Read individual data, excluding income and employment data - `employment`: Read individual employment and income data - `payment`: Read payroll and contractor related payments by the company - `pay_statement`: Read detailed pay statements for each individual - `benefits`: Create and manage deductions and contributions and enrollment for an employer [](https://god.gw.postman.com/run-collection/21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4?action=collection%2Ffork&collection-url=entityId%3D21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4%26entityType%3Dcollection%26workspaceId%3D1edf19bc-e0a8-41e9-ac55-481a4b50790b)

This project aims to build a command line tool that can create HTML view with syntax highlighted source code. It uses Jedit syntax highlighting engine and support all languages that are supported in JEdit. Which are currently: ActionScript, Ada 95, ANTLR, Apache HTTPD, APDL, AppleScript, ASP, Aspect-J, Assembly, AWK, B formal method, Batch, BBj, BCEL, BibTeX, C, C++, C#, CHILL, CIL, COBOL, ColdFusion, CSS, CVS Commit, D, DOxygen, DSSSL, Eiffel, EmbPerl, Erlang, Factor, Fortran, Foxpro, FreeMarker, Fortran, Gettext, Groovy, Haskell, HTML, Icon, IDL, Inform, INI, Inno Setup, Informix 4GL, Interlis, Io, Java, JavaScript, JCL, JHTML, JMK, JSP, Latex, Lilypond, Lisp, LOTOS, Lua, Makefile, Maple, ML, Modula-3, MoinMoin, MQSC, NetRexx, NQC, NSIS2, Objective C, ObjectRexx, Occam, Omnimark, Parrot, Pascal, Patch, Perl, PHP, Pike, PL-SQL, PL/I, Pop11, PostScript, Povray, PowerDynamo, Progress 4GL, Prolog, Properties, PSP, PV-WAVE, Pyrex, Python, REBOL, Redcode, Relax-NG, RelationalView, Rest, Rib, RPM spec, RTF, Ruby, Ruby-HTML, RView, S+, S#, SAS, Scheme, SDL/PL, SGML, Shell Script, SHTML, Smalltalk, SMI MIB, SQR, Squidconf, SVN Commit, Swig, TCL, TeX, Texinfo, TPL, Transact-SQL, UnrealScript, VBScript, Velocity, Verilog, VHDL, XML, XSL, ZPT

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classification and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from existing categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from exisiting categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.