Related Products
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About
Enjoy the highest performance and unlimited possibilities when working with SQL Server. SQL Server Data Access Components (SDAC) is a library of components that provides native connectivity to SQL Server from Delphi and C++Builder including Community Edition, as well as Lazarus (and Free Pascal) for Windows, Linux, macOS, iOS, and Android for both 32-bit and 64-bit platforms. SDAC-based applications connect to SQL Server directly through OLE DB, which is a native SQL Server interface. SDAC is designed to help programmers develop faster and cleaner SQL Server database applications. SDAC, a high-performance, and feature-rich SQL Server connectivity solution is a complete replacement for standard SQL Server connectivity solutions and presents an efficient native alternative to the Borland Database Engine (BDE) and standard dbExpress driver for access to SQL Server. SDAC-based DB applications are easy to deploy, and do not require the installation of other data provider layers.
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About
TRichView is a suite of native Delphi/C++Builder VCL/FireMonkey and Lazarus (Free Pascal) LCL components for displaying, editing, and printing complex rich text documents. TRichView can be used to develop advanced text editors, web/help/book authoring applications, chats and messengers, organizers and diaries, multimedia encyclopedias, and other applications that need a high-quality rich text editor or a hypertext user interface. Supported FireMonkey platforms, Windows, 64-bit macOS. The components support various character attributes (fonts, subscripts/superscripts, colored text background, custom drawn). Documents can contain tables, pictures, images from image lists, footnotes and endnotes, and any Delphi controls. Left, right, center, or justify paragraph alignment, custom margins and indents, multilevel bullets and numbering, background images, print preview, data-aware versions, and more.
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About
Word2Vec is a neural network-based technique for learning word embeddings, developed by researchers at Google. It transforms words into continuous vector representations in a multi-dimensional space, capturing semantic relationships based on context. Word2Vec uses two main architectures: Skip-gram, which predicts surrounding words given a target word, and Continuous Bag-of-Words (CBOW), which predicts a target word based on surrounding words. By training on large text corpora, Word2Vec generates word embeddings where similar words are positioned closely, enabling tasks like semantic similarity, analogy solving, and text clustering. The model was influential in advancing NLP by introducing efficient training techniques such as hierarchical softmax and negative sampling. Though newer embedding models like BERT and Transformer-based methods have surpassed it in complexity and performance, Word2Vec remains a foundational method in natural language processing and machine learning research.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Programmers in need of a tool to develop faster and cleaner SQL Server database applications
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Audience
Anyone seeking a solution for displaying, editing and printing complex rich text documents
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Audience
Researchers, data scientists, and developers working in natural language processing (NLP) and machine learning who need efficient word embeddings for text analysis and semantic understanding
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
Screenshots and VideosNo images available
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Pricing
$199.95 per year
Free Version
Free Trial
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Pricing
€310 one-time payment
Free Version
Free Trial
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Pricing
Free
Free Version
Free Trial
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Reviews/
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationDevart
Founded: 1997
Czech Republic
www.devart.com/sdac/
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Company InformationTRichView
Russia
www.trichview.com/features/trichview.html
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Company InformationGoogle
Founded: 1998
United States
code.google.com/archive/p/word2vec/
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Categories |
Categories |
Categories |
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Integrations
Azure SQL Database
C++Builder
Delphi
FreeBSD
Gensim
Pascal
RAD Studio
SQL Server
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Integrations
Azure SQL Database
C++Builder
Delphi
FreeBSD
Gensim
Pascal
RAD Studio
SQL Server
|
Integrations
Azure SQL Database
C++Builder
Delphi
FreeBSD
Gensim
Pascal
RAD Studio
SQL Server
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