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

Wing Python IDE was designed from the ground up for Python, to bring you a more productive development experience. Type less and let Wing worry about the details. Get immediate feedback by writing your Python code interactively in the live runtime. Easily navigate code and documentation. Avoid common errors and find problems early with assistance from Wing's deep Python code analysis. Keep code clean with smart refactoring and code quality inspection. Debug any Python code. Inspect debug data and try out bug fixes interactively without restarting your app. Work locally or on a remote host, VM, or container. Wingware's 21 years of Python IDE experience bring you a more Pythonic development environment. Wing was designed from the ground up for Python, written in Python, and is extensible with Python. So you can be more productive.

About

Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.

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

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

Python developers seeking a tool to build applications

Audience

Engineers and data scientists requiring a solution to manage and improve their machine learning research

Support

Phone Support
24/7 Live Support
Online

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

API

Offers API

Screenshots and Videos

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

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 4.3 / 5
features 5.0 / 5
design 5.0 / 5
support 5.0 / 5

Reviews/Ratings

Overall 3.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 1.0 / 5
support 4.0 / 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

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

Wingware
Founded: 1999
United States
wingware.com

Company Information

scikit-learn
United States
scikit-learn.org/stable/

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Replicate

Categories

Categories

Categories

Categories

Deep Learning Features

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

Application Development Features

Access Controls/Permissions
Code Assistance
Code Refactoring
Collaboration Tools
Compatibility Testing
Data Modeling
Debugging
Deployment Management
Graphical User Interface
Mobile Development
No-Code
Reporting/Analytics
Software Development
Source Control
Testing Management
Version Control
Web App Development

Integrations

Activeeon ProActive
AssurX
AuraQuantic
C
DagsHub
Databricks Data Intelligence Platform
Django
Docker
Equinox
Guild AI
Intel Tiber AI Studio
Keras
ModelOp
NumPy
Python
Raspberry Pi OS
Spyder
Train in Data
Vagrant
Wing

Integrations

Activeeon ProActive
AssurX
AuraQuantic
C
DagsHub
Databricks Data Intelligence Platform
Django
Docker
Equinox
Guild AI
Intel Tiber AI Studio
Keras
ModelOp
NumPy
Python
Raspberry Pi OS
Spyder
Train in Data
Vagrant
Wing

Integrations

Activeeon ProActive
AssurX
AuraQuantic
C
DagsHub
Databricks Data Intelligence Platform
Django
Docker
Equinox
Guild AI
Intel Tiber AI Studio
Keras
ModelOp
NumPy
Python
Raspberry Pi OS
Spyder
Train in Data
Vagrant
Wing

Integrations

Activeeon ProActive
AssurX
AuraQuantic
C
DagsHub
Databricks Data Intelligence Platform
Django
Docker
Equinox
Guild AI
Intel Tiber AI Studio
Keras
ModelOp
NumPy
Python
Raspberry Pi OS
Spyder
Train in Data
Vagrant
Wing
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