10 Top Linux Open-source AI tools

In this article, we'll cover several top-of-the-box AI tools for the open source Linux ecosystem. AI is currently one of the areas of progress in science and technology, and many people are working to build software and hardware to address everyday challenges in areas such as healthcare, education, security, manufacturing, banking, and more.

Here are a series of platforms designed and developed to support AI, allowing you to use Linux or possibly many other operating systems. The order of the list is meaningless.

1.Deep Learning for Java (deeplearning4j)

Deeplearning4j is a Java and Scala programming language, commercial-grade, open-source, plug-and-play, distributed deep Learning Library. It is designed for enterprise-related applications and inherits Hadoop and spark on the basis of distributed CPUs and GPUs.

DL4J is released under the Apache 2.0 license, provides GPU support for AWS, and is suitable for microservices architectures.

Official website: http://deeplearning4j.org/

2.caffe--Deep Learning Framework

Caffe is a modular and expressive deep learning framework based on speed. It was released under the BSD 2-clause license and has supported a number of community projects in industrial applications such as research, startup prototyping, and visual, voice and multimedia.

Official website: http://caffe.berkeleyvision.org/

3. h20--Distributed machine Learning Framework

H20 is an open-source, fast, extensible and distributed machine learning framework, as well as a framework-equipped algorithm. It supports smarter applications such as deep learning, gradient boosting, random forests, generalized linear models (i.e. logistic regression, elastic networks) and so on.

This is a business-oriented AI tool for decision data that enables users to draw insights from their data using faster and better predictive models.

Official website: http://www.h2o.ai/

4. mllib--Machine Learning Library

Mllib is an open source, easy-to-use and high-performance machine learning Library, developed as part of Apache Soark. It is inherently easy to deploy and can run on existing Hadoop clusters and data.

Mllib also comes with a collection of algorithms, such as classification, regression, recommendation, clustering, survival analysis, and so on. Importantly, it can be used in Python, Java, Scala, and R programming languages.

Official website: https://spark.apache.org/mllib/

5.Apache Mahout

Mahout is an open source framework designed to build scalable machine learning applications with the following three notable features:

• Provides a simple and extensible programming workplace
• Provides a variety of pre-packaged algorithms for scala+ Apache SPARK,H20 and Apache Flik
• including Samaras, Vector math experiments work places with syntax such as R

Official website: http://mahout.apache.org/

6.Open Neural Networks Library (opennn)

OPENNN is also an open-source class library for deep learning, written in C + +, to incite neural networks. However, it is just the best choice for experienced C + + programmers and people with extremely high machine learning skills. It focuses on deep architecture and high performance.

Official website: http://www.opennn.net/

7. Oryx 2

Oryx 2 is a continuation of the initial Oryx project, developed as a re-architecture of the lambda architecture on the basis of Apache Spark and Apache Kafka, although dedicated to real-time machine learning.

It is an application development and a platform that comes with some applications for collaborative filtering, classification, regression, and clustering purposes.

Official website: http://oryx.io/

8. OpenCyc

OPENCYC is an open source portal for the largest and most comprehensive common knowledge base and common sense reasoning engine. It consists of a large number of CYC terms, arranged in a precisely designed way, in areas such as the application:

• Rich Field Modeling
• Expert systems in specific fields
• Understanding of the text
• Semantic data integration, AI games, and more.

Official website: http://www.cyc.com/platform/opencyc/

9.Apache SYSTEMML

SYSTEMML is an open-source AI platform for machine learning that is ideal for big data. Its main feature is that it runs on the syntax of R and Python, focusing on big data and specifically designed for high-level math. How it works on the home page has a good explanation, including a clear description of the video presentation.

There are several ways to use it, including Apache Spark, Apache Hadoop, Jupyter, and Apache Zeppelin. Some notable uses include automobiles, airport transportation and social banking.

Official website: http://systemml.apache.org/

Ten. Nupic

Nupic is an open source framework for machine learning, based on the heirarchical temporary Memory (HTM), a new cortical theory. The Nupic-integrated HTM program implements the analysis of real-time streaming data, where it learns time-based patterns of existing data, predicts impending values, and reveals any irregular behavior.

Notable features include:

• Continuous online learning
• Spatio-Temporal pattern
• Live Streaming data
• Forecasting and modeling
• Powerful anomaly detection
• Layered Time Memory

Official website: http://numenta.org/

As AI research advances and continues to evolve, we are bound to witness the emergence of more tools that help to succeed in this technological field, especially in addressing everyday scientific challenges and for educational purposes.

Are you interested in AI, do you have anything to say? You are welcome to provide your ideas.

Link: http://www.codeceo.com/article/10-top-linux-ai-tools.html
English Original: Top Open Source Artificial Intelligence Tools for Linux
Translation Code Agricultural Network-Xiao Feng

10 Top Linux Open-source AI tools