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Description

Currents News API revolutionizes the way developers access global news by aggregating over 90,000 articles each day from 70 different countries in 18 languages, while also maintaining an extensive archive of over 26 million articles spanning four years, all organized into structured JSON through its proprietary ExtractNet deep-learning extraction technology. The service meticulously monitors 43,552 domains to guarantee wide-ranging coverage, features natural-language and SQL-like query options for targeted keyword or phrase searches, and includes built-in analytical tools to highlight insights and emerging trends. With a robust 99.9% SLA, support for CORS to facilitate browser-based applications, and the elimination of the need for users to manage crawlers, Currents efficiently oversees data sourcing and endpoint management, ensuring the delivery of news rich in content, diverse perspectives, and unique metadata scores. Users can easily access the API through straightforward RESTful endpoints or API calls, enabling them to filter news by language, region, and topic, compare viewpoints across various political and cultural dimensions, and seamlessly integrate both real-time and historical news into their applications. This makes Currents not only a powerful tool for developers but also a reliable source for comprehensive news coverage.

Description

DL4J leverages state-of-the-art distributed computing frameworks like Apache Spark and Hadoop to enhance the speed of training processes. When utilized with multiple GPUs, its performance matches that of Caffe. Fully open-source under the Apache 2.0 license, the libraries are actively maintained by both the developer community and the Konduit team. Deeplearning4j, which is developed in Java, is compatible with any language that runs on the JVM, including Scala, Clojure, and Kotlin. The core computations are executed using C, C++, and CUDA, while Keras is designated as the Python API. Eclipse Deeplearning4j stands out as the pioneering commercial-grade, open-source, distributed deep-learning library tailored for Java and Scala applications. By integrating with Hadoop and Apache Spark, DL4J effectively introduces artificial intelligence capabilities to business settings, enabling operations on distributed CPUs and GPUs. Training a deep-learning network involves tuning numerous parameters, and we have made efforts to clarify these settings, allowing Deeplearning4j to function as a versatile DIY resource for developers using Java, Scala, Clojure, and Kotlin. With its robust framework, DL4J not only simplifies the deep learning process but also fosters innovation in machine learning across various industries.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Apache Spark No 
Hadoop No 
JSON Yes 

Integrations

Apache Spark Yes 
Hadoop Yes 
JSON No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Currents News API

Founded

2019

Country

United States

Website

currentsapi.services/

Vendor Details

Company Name

Deeplearning4j

Founded

2019

Country

Japan

Website

deeplearning4j.org

Product Features

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

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