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About

Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.

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

The Universal Sentence Encoder (USE) encodes text into high-dimensional vectors that can be utilized for tasks such as text classification, semantic similarity, and clustering. It offers two model variants: one based on the Transformer architecture and another on Deep Averaging Network (DAN), allowing a balance between accuracy and computational efficiency. The Transformer-based model captures context-sensitive embeddings by processing the entire input sequence simultaneously, while the DAN-based model computes embeddings by averaging word embeddings, followed by a feedforward neural network. These embeddings facilitate efficient semantic similarity calculations and enhance performance on downstream tasks with minimal supervised training data. The USE is accessible via TensorFlow Hub, enabling seamless integration into various applications.

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

Component Library solution for DevOps teams

Audience

Developers interested in a beautiful but advanced programming language

Audience

Data scientists and machine learning engineers seeking a tool to optimize their natural language processing models with robust sentence embeddings

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

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

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 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:

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Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

NumPy
numpy.org

Company Information

Python
Founded: 1991
www.python.org

Company Information

Tensorflow
Founded: 2015
United States
www.tensorflow.org/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder

Alternatives

Alternatives

Alternatives

word2vec

word2vec

Google
h5py

h5py

HDF5
Exa

Exa

Exa.ai

Categories

Categories

Categories

Integrations

AIOHTTP
AnyIP
ApiFlash
Arachnophilia
Arm MAP
Cognee
DWSIM
Diaflow
Gensim
Glipper
Howdy
IronPython
JetBrains Aqua
LangGraph
Red Hat AMQ
Service Objects Email Validation
Spyder
StarCoder
Zebedee
Zepl

Integrations

AIOHTTP
AnyIP
ApiFlash
Arachnophilia
Arm MAP
Cognee
DWSIM
Diaflow
Gensim
Glipper
Howdy
IronPython
JetBrains Aqua
LangGraph
Red Hat AMQ
Service Objects Email Validation
Spyder
StarCoder
Zebedee
Zepl

Integrations

AIOHTTP
AnyIP
ApiFlash
Arachnophilia
Arm MAP
Cognee
DWSIM
Diaflow
Gensim
Glipper
Howdy
IronPython
JetBrains Aqua
LangGraph
Red Hat AMQ
Service Objects Email Validation
Spyder
StarCoder
Zebedee
Zepl
Claim NumPy and update features and information
Claim NumPy and update features and information
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Claim Python and update features and information
Claim Universal Sentence Encoder and update features and information
Claim Universal Sentence Encoder and update features and information