word2vec

word2vec

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About

Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Nim generates native dependency-free executables, not dependent on a virtual machine, which are small and allow easy redistribution. Nim's memory management is deterministic and customizable with destructors and move semantics, inspired by C++ and Rust. It is well-suited for embedded, hard-realtime systems. Modern concepts like zero-overhead iterators and compile-time evaluation of user-defined functions, in combination with the preference of value-based datatypes allocated on the stack, lead to extremely performant code. Support for various backends: it compiles to C, C++ or JavaScript so that Nim can be used for all backend and frontend needs.

About

The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. Whether you're new to programming or an experienced developer, it's easy to learn and use Python. Python can be easy to pick up whether you're a first-time programmer or you're experienced with other languages. The following pages are a useful first step to get on your way to writing programs with Python! The community hosts conferences and meetups to collaborate on code, and much more. Python's documentation will help you along the way, and the mailing lists will keep you in touch. The Python Package Index (PyPI) hosts thousands of third-party modules for Python. Both Python's standard library and the community-contributed modules allow for endless possibilities.

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.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Programming Language solution for DevOps

Audience

Developers interested in a beautiful but advanced programming language

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

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

No images available

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 5.0 / 5
support 5.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Nim
nim-lang.org

Company Information

Python
Founded: 1991
www.python.org

Company Information

Google
Founded: 1998
United States
code.google.com/archive/p/word2vec/

Alternatives

D

D

D Language Foundation

Alternatives

Alternatives

Zig

Zig

Zig Software Foundation
Gensim

Gensim

Radim Řehůřek
GloVe

GloVe

Stanford NLP
Apache Groovy

Apache Groovy

The Apache Software Foundation
Dart

Dart

Dart Language
LexVec

LexVec

Alexandre Salle

Categories

Categories

Categories

Integrations

Athina AI
Augoor
Avanzai
Batteries Included
Betterscan.io
Clutch
Cython
Death By Captcha
Editor.do
Electrum-LTC
Exceptionly
FOSSA
Gemini 2.5 Flash
JSON Formatter
LeaderGPU
MERCI Cloud ERP
UbiOps
dstack
scikit-image
zope.interface

Integrations

Athina AI
Augoor
Avanzai
Batteries Included
Betterscan.io
Clutch
Cython
Death By Captcha
Editor.do
Electrum-LTC
Exceptionly
FOSSA
Gemini 2.5 Flash
JSON Formatter
LeaderGPU
MERCI Cloud ERP
UbiOps
dstack
scikit-image
zope.interface

Integrations

Athina AI
Augoor
Avanzai
Batteries Included
Betterscan.io
Clutch
Cython
Death By Captcha
Editor.do
Electrum-LTC
Exceptionly
FOSSA
Gemini 2.5 Flash
JSON Formatter
LeaderGPU
MERCI Cloud ERP
UbiOps
dstack
scikit-image
zope.interface
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