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About

​JAX is a Python library designed for high-performance numerical computing and machine learning research. It offers a NumPy-like API, facilitating seamless adoption for those familiar with NumPy. Key features of JAX include automatic differentiation, just-in-time compilation, vectorization, and parallelization, all optimized for execution on CPUs, GPUs, and TPUs. These capabilities enable efficient computation for complex mathematical functions and large-scale machine-learning models. JAX also integrates with various libraries within its ecosystem, such as Flax for neural networks and Optax for optimization tasks. Comprehensive documentation, including tutorials and user guides, is available to assist users in leveraging JAX's full potential. ​

About

The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK can be included as a library in your Python, C#, or C++ programs, or used as a standalone machine-learning tool through its own model description language (BrainScript). In addition you can use the CNTK model evaluation functionality from your Java programs. CNTK supports 64-bit Linux or 64-bit Windows operating systems. To install you can either choose pre-compiled binary packages, or compile the toolkit from the source provided in GitHub.

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

Professional researchers and developers searching for a solution to manage their numerical computing and machine learning operations in Python

Audience

Developers and enterprises seeking a toolkit solution designed for commercial-grade distributed deep learning

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

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
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

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

Review this Software

Reviews/Ratings

Overall 5.0 / 5
ease 4.3 / 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

JAX
United States
docs.jax.dev/en/latest/

Company Information

Microsoft
Founded: 1975
United States
docs.microsoft.com/en-us/cognitive-toolkit/

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
Apache Mahout

Apache Mahout

Apache Software Foundation
DeepSpeed

DeepSpeed

Microsoft
Neural Designer

Neural Designer

Artelnics
Gensim

Gensim

Radim Řehůřek
Exa

Exa

Exa.ai

Categories

Categories

Categories

Deep Learning Features

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Integrations

Activeeon ProActive
Alteryx
AssurX
AuraQuantic
Azure Data Science Virtual Machines
Azure Database for MariaDB
Equinox
Flower
Gemma 3n
Google Colab
Grain
Hugging Face
IREN Cloud
Keras
LiteRT
Microsoft Dynamics 365 Finance
Microsoft Dynamics Supply Chain Management
Microsoft Power Platform
NumPy
TensorFlow

Integrations

Activeeon ProActive
Alteryx
AssurX
AuraQuantic
Azure Data Science Virtual Machines
Azure Database for MariaDB
Equinox
Flower
Gemma 3n
Google Colab
Grain
Hugging Face
IREN Cloud
Keras
LiteRT
Microsoft Dynamics 365 Finance
Microsoft Dynamics Supply Chain Management
Microsoft Power Platform
NumPy
TensorFlow

Integrations

Activeeon ProActive
Alteryx
AssurX
AuraQuantic
Azure Data Science Virtual Machines
Azure Database for MariaDB
Equinox
Flower
Gemma 3n
Google Colab
Grain
Hugging Face
IREN Cloud
Keras
LiteRT
Microsoft Dynamics 365 Finance
Microsoft Dynamics Supply Chain Management
Microsoft Power Platform
NumPy
TensorFlow
Claim JAX and update features and information
Claim JAX and update features and information
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