Search Results for "learning"

Elasticsearch is a distributed, RESTful search and analytics engine that lets you store, search and analyze with ease at scale. It lets you perform and combine many types of searches; it scales seamlessly, and offers answers incredibly fast with search results you can rank based on a variety of factors. Elasticsearch can be used for a wide variety of use cases, from maps and metrics to site search and workplace search, and with all data types.

...The project targets workloads where embedding search needs to be compact, efficient, and practical to integrate into Python applications. It avoids a separate training phase for the quantizer, which can simplify setup compared with systems that require codebook learning. TurboVec is useful for developers building retrieval, ranking, semantic search, recommendation, or AI memory systems. Its main value is combining Rust performance with a Python-facing workflow for modern vector search experiments and applications.

Tracks your most used directories, based on 'frecency'. After a short learning phase, z will take you to the most 'frecent directory that matches ALL of the regexes given on the command line, in order. For example, z foo bar would match /foo/bar but not /bar/foo. The rank of directories maintained by z undergoes aging based on a simple formula. The rank of each entry is incremented every time it is accessed.

ENAS in PyTorch is a PyTorch implementation of Efficient Neural Architecture Search (ENAS), a method that automates the design of neural network architectures through reinforcement learning and parameter sharing. The repository demonstrates how a controller network can explore a large search space and discover high-performing architectures while dramatically reducing the computational cost traditionally associated with neural architecture search. It is primarily intended as a research and educational codebase, helping practitioners understand how ENAS works in practice and how to reproduce results on benchmark datasets. ...

It is a desktop search (aiming learning materials) tool that provides full text search with a friendly GUI

Didaskon will deliver a framework for assembling a curriculum from existing learning objects provided by e-Learning services. The selection of learning objects will be based on the semantically annotated specification of the user's current skills.

PALOMA Suite allows the referencing of learning resources using metadata according to the international IEEE LOM standard, and SCORM, CANCORE and Normetic application profiles. PALOMA Suite contains: PALOMA (Standard), PALOMAWeb and PALOMARepository.

The project is building an E-Learning Framework (ELF - JISC) toolkit based around an eProfile server (ELF Producer) that will maintain individuals personal profiles and will support the social networks and relationships that exist between these users.

ScenConnect shows scenarios as networks of situation and event tag sets, for fast comparisons. It links scenarios to tags, scores, and other metadata, creating situationals suitable for search, mining, machine learning, and planning.