Search Results for "construction"
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Showing 3 open source projects for "construction"
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Paessler: Easy to Use With Enterprise Power. Free TrialYou shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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LakshmiDROP
Fixed Income Analytics, Portfolio Construction, Asset Backed Cost
DROP implements the model libraries and provides systems for fixed income valuation and adjustments, asset allocation and transaction cost analytics, and supporting libraries in numerical optimization and statistical learning. DROP is composed of four main libraries: [Asset Allocation] (https://lakshmidrip.github.io/DROP/AssetAllocationModule.html) [Fixed Income Analytics] (https://lakshmidrip.github.io/DROP/FixedIncomeModule.html) [Numerical Optimization]... -
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Runtime
A Swift Runtime library for viewing type info
A Swift Runtime library for viewing type info, and the dynamic getting and setting of properties. Runtime is a Swift library to give you more runtime abilities, including getting type metadata, setting properties via reflection, and type construction for native swift objects. TypeInfo exposes metadata about native Swift structs, protocols, classes, tuples and enums. It captures the properties, generic types, inheritance levels, and more. -
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TNN
Uniform deep learning inference framework for mobile
...As a basic acceleration framework for Tencent Cloud AI, TNN has provided acceleration support for the implementation of many businesses. Everyone is welcome to participate in the collaborative construction to promote the further improvement of the TNN inference framework.
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