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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
API Access
Has API
Integrations
Amdocs Customer Experience Suite
Apache Spark
FreedomFi
Hadoop
k0rdent
Integrations
Amdocs Customer Experience Suite
Apache Spark
FreedomFi
Hadoop
k0rdent
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
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
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization