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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

Koin streamlines dependency injection (DI) and enhances the experience for developers working within the Kotlin ecosystem. It enables the creation of various applications, spanning from Android mobile to backend Ktor server implementations. Designed to meet the needs of even the most challenging applications, Koin has earned the trust of developers globally. As an open-source framework specifically crafted for Kotlin, Koin features an intuitive domain-specific language (DSL) and a lightweight container that simplifies dependency management for any Kotlin application or SDK. With Koin, developers can effortlessly set up their dependency injection through its straightforward API and Kotlin DSL, allowing them to concentrate on developing complex Android mobile applications rather than getting bogged down by their tools. This framework integrates seamlessly with the broader Kotlin ecosystem, leveraging Kotlin's capabilities to facilitate easy dependency injection across multiple platforms. Already adopted by thousands of companies worldwide, Koin is well-equipped to handle scaling in production environments while continuing to evolve with developer needs. By providing a user-friendly approach to dependency injection, Koin empowers developers to innovate and deliver high-quality applications efficiently.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Android No 
Apache Spark Yes 
Hadoop Yes 
Jetpack Compose No 
Kotlin No 
Kotzilla No 
Ktor No 
Yuno No 

Integrations

Android Yes 
Apache Spark No 
Hadoop No 
Jetpack Compose Yes 
Kotlin Yes 
Kotzilla Yes 
Ktor Yes 
Yuno Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial Yes 
Free Version Yes 

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) Yes 
In Person No 

Vendor Details

Company Name

Deeplearning4j

Founded

2019

Country

Japan

Website

deeplearning4j.org

Vendor Details

Company Name

Kotzilla

Founded

2022

Country

France

Website

kotzilla.io/koin

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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