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ease
features
design
support

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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.

Description

Communications service providers utilize Magma's open network core solution to facilitate connectivity through LTE, 5G, Wi-Fi, and additional technologies. This solution presents a cost-effective, adaptable, and commercial-grade Evolved Packet Core (EPC). Meta Connectivity actively contributes to the development of Magma, which empowers Communication Service Providers (CSPs) to offer rapid and dependable internet access, enriched with unique features that emerge from a vibrant open-source developer community. Serving as a versatile open-source software platform, Magma allows operators to efficiently establish mobile networks even in remote locations while maintaining a reasonable cost structure. By collaborating with qualified partners for the deployment and management of Magma, CSPs can be confident that their most demanding requirements will be satisfied. Notably, Magma is agnostic to vendors, hardware, and networks, enabling CSPs to select the most suitable options for their needs, ranging from radio access network (RAN) equipment to standard hardware and a variety of licensed or unlicensed spectrum options. This flexibility ensures that service providers can tailor their networks to meet the specific demands of their operational environment and customer base.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amdocs Customer Experience Suite No 
Apache Spark Yes 
FreedomFi No 
Hadoop Yes 
k0rdent No 

Integrations

Amdocs Customer Experience Suite Yes 
Apache Spark No 
FreedomFi Yes 
Hadoop No 
k0rdent Yes 

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 Yes 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Deeplearning4j

Founded

2019

Country

Japan

Website

deeplearning4j.org

Vendor Details

Company Name

Meta Platforms

Country

United States

Website

www.facebook.com/connectivity/solutions/magma

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 

Product Features

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