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  5. Lightly vs Microsoft Cognitive Services

Lightly vs Microsoft Cognitive Services

OverviewComparisonAlternatives

Overview

Microsoft Cognitive Services
Microsoft Cognitive Services
Stacks52
Followers34
Votes0
Lightly
Lightly
Stacks0
Followers1
Votes0
GitHub Stars3.6K
Forks313

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Detailed Comparison

Microsoft Cognitive Services
Microsoft Cognitive Services
Lightly
Lightly

Infuse your apps, websites and bots with intelligent algorithms to see, hear, speak, understand and interpret your user needs through natural methods of communication. Transform your business with AI today.

Data labeling is time-consuming and can be very expensive. Lightly tells companies which data to label to have the biggest impact on model accuracy while saving time and costs.

Build confidently with the first AI services to achieve human parity in computer vision, speech, and language; Apply AI to more scenarios with the most comprehensive portfolio of domain-specific AI capabilities on the market; Deploy anywhere from the cloud to the edge with containers
Save money on your data related costs by removing redundancies; Reduce overfitting and improve generalization by diversifying your dataset
Statistics
GitHub Stars
-
GitHub Stars
3.6K
GitHub Forks
-
GitHub Forks
313
Stacks
52
Stacks
0
Followers
34
Followers
1
Votes
0
Votes
0
Integrations
No integrations available
Linux
Linux
Python
Python
Mac OS X
Mac OS X

What are some alternatives to Microsoft Cognitive Services, Lightly?

Cloudinary

Cloudinary

Cloudinary is a cloud-based service that streamlines websites and mobile applications' entire image and video management needs - uploads, storage, administration, manipulations, and delivery.

imgix

imgix

imgix is the leading platform for end-to-end visual media processing. With robust APIs, SDKs, and integrations, imgix empowers developers to optimize, transform, manage, and deliver images and videos at scale through simple URL parameters.

TensorFlow

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

OpenCV

OpenCV

OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.

scikit-learn

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

ImageKit

ImageKit

ImageKit offers a real-time URL-based API for image & video optimization, streaming, and 50+ transformations to deliver perfect visual experiences on websites and apps. It also comes integrated with a Digital Asset Management solution.

Keras

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

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