Related Products
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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.
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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.
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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.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Researchers, developers and professionals requiring an open-source, distributed, deep learning library for the JVM
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Audience
Developers looking for a Programming Language solution
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Audience
Developers and enterprises seeking a toolkit solution designed for commercial-grade distributed deep learning
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
Screenshots and Videos |
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Pricing
No information available.
Free Version
Free Trial
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Pricing
Free
Free Version
Free Trial
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Pricing
No information available.
Free Version
Free Trial
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Reviews/
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationDeeplearning4j
Founded: 2019
Japan
deeplearning4j.org
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Company InformationOracle
docs.oracle.com/javase/8/docs/technotes/guides/language/index.html
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Company InformationMicrosoft
Founded: 1975
United States
docs.microsoft.com/en-us/cognitive-toolkit/
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Deep Learning Features
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
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Integrations
Bazel
DeepSeek Coder
Duckly
GPT-5
Google Cloud Run
Jaunt
ORMIT™-APEX
OpenAI o1-mini
PDFmyURL
Polar Signals
|
Integrations
Bazel
DeepSeek Coder
Duckly
GPT-5
Google Cloud Run
Jaunt
ORMIT™-APEX
OpenAI o1-mini
PDFmyURL
Polar Signals
|
Integrations
Bazel
DeepSeek Coder
Duckly
GPT-5
Google Cloud Run
Jaunt
ORMIT™-APEX
OpenAI o1-mini
PDFmyURL
Polar Signals
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