Java

Java

Oracle
MLlib

MLlib

Apache Software Foundation

About

DL4J takes advantage of the latest distributed computing frameworks including Apache Spark and Hadoop to accelerate training. On multi-GPUs, it is equal to Caffe in performance. The libraries are completely open-source, Apache 2.0, and maintained by the developer community and Konduit team. Deeplearning4j is written in Java and is compatible with any JVM language, such as Scala, Clojure, or Kotlin. The underlying computations are written in C, C++, and Cuda. Keras will serve as the Python API. Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Apache Spark, DL4J brings AI to business environments for use on distributed GPUs and CPUs. There are a lot of parameters to adjust when you're training a deep-learning network. We've done our best to explain them, so that Deeplearning4j can serve as a DIY tool for Java, Scala, Clojure, and Kotlin programmers.

About

The Java™ Programming Language is a general-purpose, concurrent, strongly typed, class-based object-oriented language. It is normally compiled to the bytecode instruction set and binary format defined in the Java Virtual Machine Specification. In the Java programming language, all source code is first written in plain text files ending with the .java extension. Those source files are then compiled into .class files by the javac compiler. A .class file does not contain code that is native to your processor; it instead contains bytecodes — the machine language of the Java Virtual Machine1 (Java VM). The java launcher tool then runs your application with an instance of the Java Virtual Machine.

About

​Apache Spark's MLlib is a scalable machine learning library that integrates seamlessly with Spark's APIs, supporting Java, Scala, Python, and R. It offers a comprehensive suite of algorithms and utilities, including classification, regression, clustering, collaborative filtering, and tools for constructing machine learning pipelines. MLlib's high-quality algorithms leverage Spark's iterative computation capabilities, delivering performance up to 100 times faster than traditional MapReduce implementations. It is designed to operate across diverse environments, running on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or in the cloud, and accessing various data sources such as HDFS, HBase, and local files. This flexibility makes MLlib a robust solution for scalable and efficient machine learning tasks within the Apache Spark ecosystem. ​

About

The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK can be included as a library in your Python, C#, or C++ programs, or used as a standalone machine-learning tool through its own model description language (BrainScript). In addition you can use the CNTK model evaluation functionality from your Java programs. CNTK supports 64-bit Linux or 64-bit Windows operating systems. To install you can either choose pre-compiled binary packages, or compile the toolkit from the source provided in GitHub.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Researchers, developers and professionals requiring an open-source, distributed, deep learning library for the JVM

Audience

Developers looking for a Programming Language solution

Audience

Data scientists and engineers wanting a machine learning solution for efficient data processing and analysis within the Apache Spark framework

Audience

Developers and enterprises seeking a toolkit solution designed for commercial-grade distributed deep learning

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5
design 5.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 5.0 / 5
ease 4.3 / 5
features 5.0 / 5
design 5.0 / 5
support 5.0 / 5

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Deeplearning4j
Founded: 2019
Japan
deeplearning4j.org

Company Information

Oracle
docs.oracle.com/javase/8/docs/technotes/guides/language/index.html

Company Information

Apache Software Foundation
Founded: 1995
United States
spark.apache.org/mllib/

Company Information

Microsoft
Founded: 1975
United States
docs.microsoft.com/en-us/cognitive-toolkit/

Alternatives

MXNet

MXNet

The Apache Software Foundation

Alternatives

Alternatives

Apache Spark

Apache Spark

Apache Software Foundation

Alternatives

Apache Mahout

Apache Mahout

Apache Software Foundation
Apache Mahout

Apache Mahout

Apache Software Foundation
MLlib

MLlib

Apache Software Foundation
Amazon EMR

Amazon EMR

Amazon
Neural Designer

Neural Designer

Artelnics
Apache Spark

Apache Spark

Apache Software Foundation

Categories

Categories

Categories

Categories

Deep Learning Features

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Integrations

Akto
Apache TomEE
CyberSiARA
DeepSeek-V3
GaraSign
Gideros
JCov
NGINX Unit
Python RPA
QuickChart
Qwen
SOOS
Snyk
Treblle
TrueZero Tokenization
Upsun
Void Editor
ZenRows
txtai
yEd

Integrations

Akto
Apache TomEE
CyberSiARA
DeepSeek-V3
GaraSign
Gideros
JCov
NGINX Unit
Python RPA
QuickChart
Qwen
SOOS
Snyk
Treblle
TrueZero Tokenization
Upsun
Void Editor
ZenRows
txtai
yEd

Integrations

Akto
Apache TomEE
CyberSiARA
DeepSeek-V3
GaraSign
Gideros
JCov
NGINX Unit
Python RPA
QuickChart
Qwen
SOOS
Snyk
Treblle
TrueZero Tokenization
Upsun
Void Editor
ZenRows
txtai
yEd

Integrations

Akto
Apache TomEE
CyberSiARA
DeepSeek-V3
GaraSign
Gideros
JCov
NGINX Unit
Python RPA
QuickChart
Qwen
SOOS
Snyk
Treblle
TrueZero Tokenization
Upsun
Void Editor
ZenRows
txtai
yEd
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