VBScript

VBScript

Microsoft
+

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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

VBScript is a programming language included with Microsoft Internet Explorer. For other browsers, contact your vendor about support. VBScript 2.0 (or later) is recommended for use with Agent. Although earlier versions of VBScript may work with Agent, they lack certain functions that you may want to use. You can download VBScript 2.0 and obtain further information on VBScript at the Microsoft Downloads site and the Microsoft VBScript site. With VBScript (2.0 or later), you can verify whether Microsoft Agent is installed by trying to create the object and checking to see if it exists. The following sample demonstrates how to check for the Agent control without triggering an auto-download of the control.

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

Audience

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

Audience

Programming Language solution for developers

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

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

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 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 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
docs.microsoft.com/en-us/windows/win32/lwef/using-vbscript

Company Information

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

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Replicate

Categories

Categories

Categories

Integrations

Activeeon ProActive
Advanced Email Parser
CodePal
DagsHub
Databricks Data Intelligence Platform
EditPlus
EmEditor
Gemma 3n
Grain
Hugging Face
IREN Cloud
Intel Tiber AI Studio
Matplotlib
Python
SlickEdit
System Frontier
TensorFlow
Train in Data
Typora

Integrations

Activeeon ProActive
Advanced Email Parser
CodePal
DagsHub
Databricks Data Intelligence Platform
EditPlus
EmEditor
Gemma 3n
Grain
Hugging Face
IREN Cloud
Intel Tiber AI Studio
Matplotlib
Python
SlickEdit
System Frontier
TensorFlow
Train in Data
Typora

Integrations

Activeeon ProActive
Advanced Email Parser
CodePal
DagsHub
Databricks Data Intelligence Platform
EditPlus
EmEditor
Gemma 3n
Grain
Hugging Face
IREN Cloud
Intel Tiber AI Studio
Matplotlib
Python
SlickEdit
System Frontier
TensorFlow
Train in Data
Typora
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