Universal Data Tool VS NumPy

Universal Data Tool

Machine learning, data labeling tool, computer vision, annotate-images, classification, dataset

NumPy

NumPy is the fundamental package for scientific computing with Python

Landing page //
2021-09-10

The Universal Data Tool (UDT) is an open-source web or downloadable tool for labeling data for usage in machine learning or data processing systems.

The Universal Data Tool supports Computer Vision, Natural Language Processing (including Named Entity Recognition and Audio Transcription) workflows.

The UDT uses an open-source data format (.udt.json / .udt.csv) that can be easily read by programs as a ground-truth dataset for machine learning algorithms.

Landing page //
2023-05-13

Getting Started with Open-Source Contribution to the Universal Data Tool

NumPy videos

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Learn NUMPY in 5 minutes - BEST Python Library!

Category Popularity

0-100% (relative to Universal Data Tool and NumPy)

NumPy

Image Annotation

100 100%

Image Annotation

0% 0

Data Science And Machine Learning

9 9%

Data Science And Machine Learning

91% 91

Data Labeling

100 100%

Data Labeling

0% 0

Data Science Tools

0 0%

Data Science Tools

100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Universal Data Tool and NumPy

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SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.

Source: kinsta.com

Top 8 Image-Processing Python Libraries Used in Machine Learning

Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.

Source: neptune.ai

Top Python Libraries For Image Processing In 2021

Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.

Source: www.analyticsvidhya.com

4 open source alternatives to MATLAB

NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...

Source: opensource.com

Social recommendations and mentions

Based on our record, NumPy seems to be more popular. It has been mentiond 111 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Universal Data Tool mentions (0)

We have not tracked any mentions of Universal Data Tool yet. Tracking of Universal Data Tool recommendations started around Mar 2021.

NumPy mentions (111)

Documenting my pin collection with Segment Anything: Part 3
NumPy: This library is fundamental for handling arrays and matrices, such as for operations that involve image data. NumPy is used to manipulate image data and perform calculations for image transformations and mask operations. - Source: dev.to / 2 days ago
Awesome List
NumPy - The fundamental package for scientific computing with Python. NumPy Documentation - Official documentation. - Source: dev.to / 7 days ago
NumPy for Beginners: A Basic Guide to Get You Started
This guide covers the basics of NumPy, and there's much more to explore. Visit numpy.org for more information and examples. - Source: dev.to / 9 days ago
2 Minutes to JupyterLab Notebook on Docker Desktop
Below is an example of a code cell. We'll visualize some simple data using two popular packages in Python. We'll use NumPy to create some random data, and Matplotlib to visualize it. - Source: dev.to / 9 months ago
Element-wise vs Matrix vs Dot multiplication
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / 3 months ago