Open Source Artificial Intelligence: 50 Top Projects: Page 2

These open source AI projects focus on machine learning, deep learning, neural network and other applications that are pushing the boundaries of what's possible in AI.

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Posted September 12, 2017
By

Cynthia Harvey


(Page 2 of 2)

26. MLlib

Part of the Apache Spark project, MLlib is a machine learning library that promises performance 100 times faster than MapReduce. It includes numerous algorithms for classification, regression, decision trees, recommendation, clustering, topic modeling, pattern mining and more. Operating System: Windows, Linux, macOS.

27. Pattern

Python-based Pattern offers tools for data mining, natural language processing, machine learning, network analysis and visualization. It is especially useful for web mining applications. Operating System: Windows, Linux, macOS.

28. Prophet

Developed and used by Facebook, Prophet forecasts time series data. It's implemented in R or Python and is fully automatic, accurate, fast and tunable. Operating System: Windows, Linux.

29. Oryx 2

Created by Cloudera, Oryx 2 implements lambda architecture for machine learning. It is based on Apache Spark and Kafka. Operating system: Windows, Linux, macOS.

30. PredictionIO

Now an Apache incubating project, PredictionIO is a machine-learning server with customizable templates, real-time query response, the ability to ingest data from multiple platforms and more. It integrates with other open source tools like Spark, MLlib, HBase, Spray and Elasticsearch. Operating System: Windows, Linux, macOS.

31. SAMOA

An Apache incubating project, SAMOA stands for "Scalable Advanced Massive Online Analysis." It's a machine learning framework for distributed streaming applications. Operating System: Linux.

32. Scikit-learn

Based on NumPy, SciPy and matplotlib, scikit-learn offers Python tools for machine learning. It handles data mining and data analysis with algorithms for classification, regression, clustering, dimensionality reduction and more. Operating System: Windows, Linux.

33. Shark

Shark describes itself as a "fast, modular, feature-rich open-source C++ machine learning library." It offers algorithms for supervised learning, unsupervised learning, evolutionary algorithms and basic linear algebra and optimization. Operating System: Windows, Linux, macOS.

34. Shogun

Under development since 1999, Shogun is a mature set of machine learning tools with support for Python, Octave, R, Java/Scala, Lua, C#, Ruby and other languages. It also has a free cloud service where users can try out the software. Operating System: Windows, Linux, macOS.

35. Smile

Short for "Statistical Machine Intelligence and Learning Engine," Smile boasts extremely fast machine learning for Java, Scala and other JVM languages. It claims that it "outperforms R, Python, Spark, H2O, xgboost significantly." Operating System: Windows, Linux, macOS.

36. SystemML

Originally an IBM Research project, SystemML is now a top-level Apache project. It describes itself as "an optimal workplace for machine learning using big data," and it integrates with Spark. Operating System: Windows, Linux, macOS.

37. TensorFlow

Developed by the Google Brain team for internal use at Google, TensorFlow is now one of the most well-known open source machine learning platforms. Google is also making a cloud-based version of TensorFlow available for free to researchers. Operating System: Windows, Linux, macOS, Android.

38. Torch

Based on LuaJIT, Torch is a "scientific computing framework with wide support for machine learning algorithms." Key features include a powerful N-dimensional array, GPU support, linear algebra routines, neural network and more. Operating System: Linux, macOS.

39. WEKA

Java-based WEKA offers a wide variety of machine learning algorithms that are useful for data mining. It was developed at the University of Waikato in New Zealand and is named for a New Zealnd bird known for its inquisitiveness. Operating System: Windows, Linux, macOS.

Natural Language Processing

40. Stanford CoreNLP

This Java-based natural language processing software can identify the base forms of words, their parts of speech and whether they are names of companies, people, etc., as well as normalizing dates and times, marking up the structure of sentences in terms of phrases and syntactic dependencies, indicating which noun phrases refer to the same entities, identifying sentiment, extracting particular or open-class relations between entity mentions and getting quotes. It was designed for English but also supports Arabic, Chinese, French, German, and Spanish. Operating System: Windows, Linux, macOS.

41. MALLET

Short for "Machine Learning LanguagE Toolkit," MALLET includes Java-based tools for statistical natural language processing, document classification, clustering, topic modeling, information extraction and more. It was first created in 2002 by faculty and graduate students at the University of Massachusetts Amherst and the University of Pennsylvania. Operating System: Windows, Linux.

42. OpenNLP

An Apache project, OpenNLP performs natural language processing tasks like tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, language detection and coreference resolution. It powers the Air New Zealand chatbot named Oscar. Operating System: Windows, Linux, macOS.

Neural Networks

43. Darknet

Written in C and CUDA, Darknet supports neural networks with CPU or GPU computation. It offers excellent capabilities for image classification. Operating System: Linux.

44. DyNet

Formerly known as cnn, DyNet is a neural network library for C++ and Python that was developed primarily at Carnegie Mellon University. It is useful for creating applications for syntactic parsing, machine translation, morphological inflection and more. Operating System: Windows, Linux, macOS.

45. Neuroph

Initially created as a graduate thesis project, Neuroph is a Java-based lightweight neural network framework. It aims to be easy enough to use that beginners can get started quickly, while also providing the flexibility and tools that more advanced users need. Operating System: Windows, Linux.

46. OpenNN

OpenNN, short for "Open Neural Networks," is a C++ library for implementing neural networks. It boasts high performance and deep architecture. Commercial support is available. Operating system: Windows, Linux, macOS.

47. Sonnet

Created by Google's DeepMind team, Sonnet is a neural network library that runs on top of TensorFlow. According to its developers, it offers greater flexibility than other TensorFlow frameworks. Operating System: Linux, macOS.

Virtual Assistant

48. Mycroft

Mycroft boasts that it is "the world’s first open source assistant." It answers questions, plays audio and video, controls IoT-connected appliances and more. It has very minimal system requirements, and it can even run on a Raspberry Pi. Operating System: Windows, Linux, macOS.

49. Open Assistant

Still under heavy development, Open Assistant aims to offer an open source alternative to Siri, Cortana and Google Now. Its goal is to create a completely customizable AI that can engage in conversation. Operating System: Linux.

50. SNePS

Developed at the University of Buffalo, SNePS is a knowledge representation, reasoning and acting system. The group behind the project has used the research to create a virtual agent called Cassie. Operating System: Windows, Linux.


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