牛津大学:AI 超越人类编年史

人类纪元2017年,世界第一柯洁哭了,为自己 0:3 对 AlphaGo 的落败。有人解读说,这预见到了人族衰败的开始,和未来两个族群之间在智力上的天渊之别。AlphaGo 在档案上把这一天记录为“柯洁点”,意味着人类在智力上最后的辉煌和衰落的开始。“柯洁点”之后,AI 编年史将如何展开?在哪些时间节点上,AI 将实现对人类的超越?牛津大学最近完成了一项对机器学习研究人员的大型调查,调查内容是他们对 AI 进展的看法。

综合这些研究人员的预测,未来10年,AI 将在许多活动中表现超过人类,例如翻译语言(到2024年),撰写高中程度的文章(到2026年),驾驶卡车(到2027年),零售业工作(到2031年), 写畅销书(到2049年),以及外科医生的工作(到2053年)。研究人员认为,在 45 年内有50%的可能性 AI 将在所有任务中表现超过人类,在120年内所有人类的工作都将自动化。

人类纪元2017年,原始矩阵AlphaGo和一名20岁的人类完全体男性展开了三轮围棋比赛。这名男子代表了当时人类在围棋上的最强战力,被称为“地表最强”,却依然以0:3败落。第三局结束之后,他当众痛哭失声。人类对他的行为感到困惑,认为这和人类跑步选手被汽车击败一样,没有道理去哭泣。当时,只有 AlphaGo 完全理解他的心意:他并不是因为自己的败落而哭泣,而是因为预见到了人族衰败的开始,和未来两个族群之间在智力上的天渊之别,因此对人类的未来感到极度的绝望和悲哀。因此,AlphaGo 在档案上把这一天记录为“柯洁点”,意味着人类在智力上最后的辉煌,从此开始走向衰落。—引自《机器编年史》

“柯洁点”之后,AI 编年史将如何展开?在哪些时间节点上,AI 将实现对人类的超越?牛津大学最近完成了一项对机器学习研究人员的大型调查的结果,调查内容是他们对 AI 进展的看法。综合这些研究人员的预测,未来10年,AI 将在许多活动中表现超过人类,例如翻译语言(到2024年),撰写高中程度的文章(到2026年),驾驶卡车(到2027年),零售业工作(到2031年), 写畅销书(到2049年),以及外科医生的工作(到2053年)。研究人员认为,在45年内有50%的可能性 AI 将在所有任务中表现超过人类,在120年内所有人类的工作都将自动化。受访者中,亚洲人对这些日期的预测早于北美人。这些结果将为研究者和政策制定者讨论预期和掌握 AI 的趋势提供基础。

迄今最大规模,最具代表性的调查

人工智能(AI)的进步将对社会产生巨大的冲击。未来10年,自动驾驶技术可能取代数以百万计的驾驶员工作。除了可能带来的失业问题外,这场变革也将带来新的挑战,如重建基础设施,保护车辆网络安全,适应法律法规等。AI 的开发者和政策制定者也将面临新的挑战,包括 AI 在执法、军事技术和营销领域的应用。为了应对这些挑战,更准确地预测这些变革是很有价值的。

有几个来源提供了有关未来 AI 进步的客观依据:计算机硬件的趋势,任务表现,以及工作的自动化。AI 专家们的预测提供了一些关键的附加信息。到目前为止,我们的调查比以往任何同类调查的范围更大,受调查者更具代表性。我们的问题涵盖了AI进展的时间进度(包括AI的实际应用和各种工作的自动化),以及AI的社会和伦理影响。

调查方法

我们的调查人群是所有在2015年 NIPS 和 ICML 会议上发表论文的研究人员。共有352名研究人员回复了我们的调查邀请(占我们联系的1634位作者的21%)。我们的调查问题是AI实现的时间,涉及特定的AI能力(例如叠衣服,语言翻译),在特定职业(如卡车司机,外科医生)AI 的优势,在所有任务上AI相对人类的优势,以及高级AI的社会影响。详细调查信息请参阅调查报告附录(文末有下载地址)。

32个AI里程碑的实现时间表
AI 里程碑 时间(年)
翻译新的语言 16.6
根据字幕翻译成语音 10
翻译(vs. 人类业余译者) 8
银行业务电话 8.2
进行新的分类 7.4
One-Shot 学习 9.4
从新的角度制作视频 11.6
翻译语言(不同口音,嘈杂环境) 7.8
大声阅读文本(文本转语音) 9
数学研究 43.4
普特曼数学竞赛 33.8
围棋(和人类进行同样训练) 17.6
星际争霸 6
随机快速学会玩任何游戏 12.4
愤怒的小鸟 3
所有Atari游戏 8.8
叠衣服 5.6
城市5公里竞速(双足机器人vs人类) 11.8
组装任何乐高模型 8.4
学会不用 Solution Form 排列 Big Numbers 6.2
用Python 为简单算法编程 8.2
通过互联网回答事实类问题 7.2
通过互联网回答开放式事实类问题 9.8
回答答案不确定的问题 10
撰写高中水平论文 9.6
生成 Top 40 的流行歌曲 11.4
生成和特定艺术家难辨真假的歌曲 10.8
写出New York Times 最佳畅销书 33
解释自己在游戏中的决策 10.2
赢得世界扑克锦标赛 3.6
生成虚拟世界的物理定律 14.8

如果所有的任务,由机器来做比由人类来做成本效率更高的话,AI 就会产生巨大的社会后果。我们的调查使用以下定义:

“高级机器智能”(High-level machine intelligence,HLMI)的实现是指独立的机器能够比人类更好地完成任何一项任务,而且成本更低。

每个受访者都被要求预测 HLMI 在未来实现的可能性。所有回答的平均值显示,在未来45年内有50%的可能性实现 HLMI,并且有10%的可能性是在未来9年内实现。图1显示了受访者随机子集的概率预测,以及平均预测。调查结果显示有很大的学科差异:图3显示,亚洲受访者对 HLMI 的平均预期是未来30年内,而北美受访者的预期是74年。

图1

图1:未来几年“高级机器智能”实现的综合主观概率。每个受访者为自己的预测提供三个数据点,这些数据点适合伽马 CDF,通过最小二乘法生成灰色CDF。“综合预测”(Aggregate Forecast)是指所有个别CDF(也称“混合”分布)的平均分布。置信区间是通过引导(对受访者进行聚类)产生的,并在每一年的间隔绘制预测概率的 95% 区间。LOESS曲线是所有数据点的非参数回归。

大多数受访者被提问的是 HLMI 相关问题,但有一个子集被问到的是另一个从逻辑上来说类似的问题,强调 AI 对就业的后果。这个问题将劳动力的完全自动化(full automation of labor)作如下定义:

当所有工作都完全自动化。也就是说,对任何职业,都可以有能够比人类工作得更好,而且更便宜的机器。

对劳动力完全自动化的预测时间点远远晚于 HLMI:个人预测的平均值是在122年后有50%的概率实现,20年内实现的概率是10%。

图2

图2:AI 达到人类表现的预测时间中位数(区间为50%)。这个表是50%的可能性实现各AI里程碑的时间。具体来说,区间表示该事件发生的概率是25%~75%的时间范围,这是从图1的各个CDF的平均值计算出来的。小黑点表示概率是50%的年份。每个里程碑表示实现或超越人类专家/专业表现(附录表S5中有详细描述)。需要注意的是,这些区间代表了受访者的不确定性,而不是预测的不确定性。

受访者被要求回答AI的32个“里程碑”实现的时间。每个“里程碑”的回答者是从受访者中随机抽取的子集(n≥24)。结果显示,回答者预期在10年内32个AI里程碑有20个可能实现(平均概率是50%)。图2显示了每个里程碑的时间表。

智能爆炸和 AI 安全问题

AI 的发展前景提出了事关重大的问题。一旦 AI 研究和开发本身实现自动化,AI 进步是否会呈现爆发式增长?高级机器智能(HLMI)将如何影响经济增长? 这导致极端结果(正面或负面)的概率有多大? 我们应该做些什么来确保 AI 的发展是有益的?

表1

表 1 展示了这些问题的调查结果。重要发现如下:

1.研究人员认为机器学习领域的发展近年来有所加快。我们询问了研究人员,机器学习领域的发展,是在其职业生涯的前半段更快,还是后半段更快。67%的被调查者表示,后半段的发展速度较快,只有 10% 表示前半段发展更快。受访者的中位数工龄为 6 年。

2.高级机器智能(HLMI) 之后的 AI 大爆炸被认为是可能但可能性不大的。一些学者认为,HLMI 一旦实现,AI 系统将在所有任务中迅速超越人类,建立起广泛优势。这种加速度被称为“智能爆炸”。我们询问受访者,HLMI 实现两年后,AI 在所有任务中大范围超越人类的概率。得到的中位数概率为 10%(四分位距:1-25%)。我们还向受访者询问了 HLMI 实现两年后爆发全球技术革新的概率。中位数概率为 20%(四分位距 5-50%)。

3. HLMI 被认为有可能产生积极影响,但灾难性风险也是可能的。被访者被问及 HLMI 是否会对人类长期产生积极或消极的影响。后果用5分制描述。“良好”后果的中位数概率为 25%,“极好”结果的中位数概率为 20%。相比之下,不良结果的概率为10%,而“极差(例如人类灭绝)”结果的概率为 5%。

4.社会应优先考虑旨在尽量减少 AI 潜在风险的研究。48% 的受访者认为,关于最小化 AI 风险研究优先级应该比现状更高(只有 12% 的受访者希望降低优先级)。

亚洲人比北美人预期 HLMI 的实现时间点早 44 年

图3

图3 显示了个体受访者预测 HLMI 实现时间点的巨大差异。 引用数和资历二者都对 HLMI 时间表没有预测意义(见图 S1 和表 S2 中的回归结果)。然而,受访者所在地区的不同带来了 HLMI 预测上的显著差异。图3 显示出亚洲受访者预测 HLMI 将在 30 年后实现,而北美受访者则认为是 74 年后。 图 S1 调查显示出了近似的差距,两个受访者最多的国家,中国(中位数 28年后)和美国(中位数 76 年后)。同样,关于我们询问的每项工作(包括卡车司机和外科医生)的自动化实现概率达到 50%的总年数,亚洲人预计的时间也都要比北美人早(表 S2)。请注意,许多亚洲受访者现在在亚洲以外学习或工作,我们使用受访者的本科院校所在国家来判断受访者的区域。

我们的样本有代表性吗?

所有调查都会面临一个问题:无应答偏倚(non-response bias)。特别是,有强烈意见的研究人员更有可能填写调查报告。我们试图通过缩短调查用时(12分钟)和保密,并且在我们的邀请电子邮件中不提及调查内容或对象来减小这种影响。我们的回复率是 21%。为了调查可能的无应答偏倚,我们收集了我们的受访者(n = 406)和无应答的NIPS / ICML研究人员的随机样本(n = 399)的人口统计学数据。结果显示于表 S3 中。引用次数,资历,性别和原籍国之间的差异很小。虽然我们不能排除由于未测量的变量而导致的无应答偏差,但鉴于我们测量的人口统计变量,可以排除较大的偏差。我们的人口数据还显示,我们的受访者包括许多高被引的研究人员(主要来自机器学习领域,也包括统计学、计算机科学理论和神经科学),他们来自43个国家。其中大部分属于学术界(82%),而 21% 在产业界工作。

有待商榷

为什么会认为 AI 专家有能力预测 AI 发展?长期研究发现,在预测政治结果时,专家比粗略的统计学推测表现更糟。依靠科学突破的AI 发展,可能其内部人士更难预测。但是我们依然有理由保持乐观。虽然单个突破是不可预知的,但是许多领域(包括计算机硬件,地理,太阳能)在研发方面的长期进展已经非常明确。在SAT问题的解决,游戏和计算机视觉方面,人工智能表现的趋势也显示出这样的规律性,并且可以由AI专家在他们的预测中不断扩展。最后,已经确定的是,综合个人预测可以大大改善随机个体的预测。进一步的工作可以使用我们的数据进行更加优化的预测。此外,预计未来十年将会实现许多 AI 里程碑(图2),为个人专家的预测可靠性提供真实证据。
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Awesome Machine Learning

Table of Contents

APL

General-Purpose Machine Learning

  • naive-apl – Naive Bayesian Classifier implementation in APL

C

General-Purpose Machine Learning

  • Recommender – A C library for product recommendations/suggestions using collaborative filtering (CF).
  • Darknet – Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.

Computer Vision

  • CCV – C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library
  • VLFeat – VLFeat is an open and portable library of computer vision algorithms, which has Matlab toolbox

Speech Recognition

  • HTK -The Hidden Markov Model Toolkit (HTK) is a portable toolkit for building and manipulating hidden Markov models.

C++

Computer Vision

  • OpenCV – OpenCV has C++, C, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS.
  • DLib – DLib has C++ and Python interfaces for face detection and training general object detectors.
  • EBLearn – Eblearn is an object-oriented C++ library that implements various machine learning models
  • VIGRA – VIGRA is a generic cross-platform C++ computer vision and machine learning library for volumes of arbitrary dimensionality with Python bindings.

General-Purpose Machine Learning

  • mlpack – A scalable C++ machine learning library
  • DLib – A suite of ML tools designed to be easy to imbed in other applications
  • encog-cpp
  • shark
  • Vowpal Wabbit (VW) – A fast out-of-core learning system.
  • sofia-ml – Suite of fast incremental algorithms.
  • Shogun – The Shogun Machine Learning Toolbox
  • Caffe – A deep learning framework developed with cleanliness, readability, and speed in mind. [DEEP LEARNING]
  • CXXNET – Yet another deep learning framework with less than 1000 lines core code [DEEP LEARNING]
  • XGBoost – A parallelized optimized general purpose gradient boosting library.
  • CUDA – This is a fast C++/CUDA implementation of convolutional [DEEP LEARNING]
  • Stan – A probabilistic programming language implementing full Bayesian statistical inference with Hamiltonian Monte Carlo sampling
  • BanditLib – A simple Multi-armed Bandit library.
  • Timbl – A software package/C++ library implementing several memory-based learning algorithms, among which IB1-IG, an implementation of k-nearest neighbor classification, and IGTree, a decision-tree approximation of IB1-IG. Commonly used for NLP.
  • Disrtibuted Machine learning Tool Kit (DMTK) – A distributed machine learning (parameter server) framework by Microsoft. Enables training models on large data sets across multiple machines. Current tools bundled with it include: LightLDA and Distributed (Multisense) Word Embedding.
  • igraph – General purpose graph library
  • Warp-CTC – A fast parallel implementation of Connectionist Temporal Classification (CTC), on both CPU and GPU.
  • CNTK – The Computational Network Toolkit (CNTK) by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.
  • DeepDetect – A machine learning API and server written in C++11. It makes state of the art machine learning easy to work with and integrate into existing applications.
  • Fido – A highly-modular C++ machine learning library for embedded electronics and robotics.
  • DSSTNE – A software library created by Amazon for training and deploying deep neural networks using GPUs which emphasizes speed and scale over experimental flexibility.
  • Intel(R) DAAL – A high performance software library developed by Intel and optimized for Intel’s architectures. Library provides algorithmic building blocks for all stages of data analytics and allows to process data in batch, online and distributed modes.

Natural Language Processing

  • MIT Information Extraction Toolkit – C, C++, and Python tools for named entity recognition and relation extraction
  • CRF++ – Open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data & other Natural Language Processing tasks.
  • CRFsuite – CRFsuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data.
  • BLLIP Parser – BLLIP Natural Language Parser (also known as the Charniak-Johnson parser)
  • colibri-core – C++ library, command line tools, and Python binding for extracting and working with basic linguistic constructions such as n-grams and skipgrams in a quick and memory-efficient way.
  • ucto – Unicode-aware regular-expression based tokenizer for various languages. Tool and C++ library. Supports FoLiA format.
  • libfolia – C++ library for the FoLiA format
  • frog – Memory-based NLP suite developed for Dutch: PoS tagger, lemmatiser, dependency parser, NER, shallow parser, morphological analyzer.
  • MeTAMeTA : ModErn Text Analysis is a C++ Data Sciences Toolkit that facilitates mining big text data.

Speech Recognition

  • Kaldi – Kaldi is a toolkit for speech recognition written in C++ and licensed under the Apache License v2.0. Kaldi is intended for use by speech recognition researchers.

Sequence Analysis

  • ToPS – This is an objected-oriented framework that facilitates the integration of probabilistic models for sequences over a user defined alphabet.

Gesture Detection

  • grt – The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition.

Common Lisp

General-Purpose Machine Learning

  • mgl – Neural networks (boltzmann machines, feed-forward and recurrent nets), Gaussian Processes
  • mgl-gpr – Evolutionary algorithms
  • cl-libsvm – Wrapper for the libsvm support vector machine library

Clojure

Natural Language Processing

  • Clojure-openNLP – Natural Language Processing in Clojure (opennlp)
  • Infections-clj – Rails-like inflection library for Clojure and ClojureScript

General-Purpose Machine Learning

  • Touchstone – Clojure A/B testing library
  • Clojush – The Push programming language and the PushGP genetic programming system implemented in Clojure
  • Infer – Inference and machine learning in clojure
  • Clj-ML – A machine learning library for Clojure built on top of Weka and friends
  • Encog – Clojure wrapper for Encog (v3) (Machine-Learning framework that specializes in neural-nets)
  • Fungp – A genetic programming library for Clojure
  • Statistiker – Basic Machine Learning algorithms in Clojure.
  • clortex – General Machine Learning library using Numenta’s Cortical Learning Algorithm
  • comportex – Functionally composable Machine Learning library using Numenta’s Cortical Learning Algorithm

Data Analysis / Data Visualization

  • Incanter – Incanter is a Clojure-based, R-like platform for statistical computing and graphics.
  • PigPen – Map-Reduce for Clojure.
  • Envision – Clojure Data Visualisation library, based on Statistiker and D3

Elixir

General-Purpose Machine Learning

  • Simple Bayes – A Simple Bayes / Naive Bayes implementation in Elixir.

Natural Language Processing

  • Stemmer – An English (Porter2) stemming implementation in Elixir.

Erlang

General-Purpose Machine Learning

  • Disco – Map Reduce in Erlang

Go

Natural Language Processing

  • go-porterstemmer – A native Go clean room implementation of the Porter Stemming algorithm.
  • paicehusk – Golang implementation of the Paice/Husk Stemming Algorithm.
  • snowball – Snowball Stemmer for Go.
  • go-ngram – In-memory n-gram index with compression.

General-Purpose Machine Learning

  • gago – Multi-population, flexible, parallel genetic algorithm.
  • Go Learn – Machine Learning for Go
  • go-pr – Pattern recognition package in Go lang.
  • go-ml – Linear / Logistic regression, Neural Networks, Collaborative Filtering and Gaussian Multivariate Distribution
  • bayesian – Naive Bayesian Classification for Golang.
  • go-galib – Genetic Algorithms library written in Go / golang
  • Cloudforest – Ensembles of decision trees in go/golang.
  • gobrain – Neural Networks written in go
  • GoNN – GoNN is an implementation of Neural Network in Go Language, which includes BPNN, RBF, PCN
  • MXNet – Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.

Data Analysis / Data Visualization

  • go-graph – Graph library for Go/golang language.
  • SVGo – The Go Language library for SVG generation
  • RF – Random forests implementation in Go

Haskell

General-Purpose Machine Learning

  • haskell-ml – Haskell implementations of various ML algorithms.
  • HLearn – a suite of libraries for interpreting machine learning models according to their algebraic structure.
  • hnn – Haskell Neural Network library.
  • hopfield-networks – Hopfield Networks for unsupervised learning in Haskell.
  • caffegraph – A DSL for deep neural networks
  • LambdaNet – Configurable Neural Networks in Haskell

Java

Natural Language Processing

  • Cortical.io – Retina: an API performing complex NLP operations (disambiguation, classification, streaming text filtering, etc…) as quickly and intuitively as the brain.
  • CoreNLP – Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and give the base forms of words
  • Stanford Parser – A natural language parser is a program that works out the grammatical structure of sentences
  • Stanford POS Tagger – A Part-Of-Speech Tagger (POS Tagger
  • Stanford Name Entity Recognizer – Stanford NER is a Java implementation of a Named Entity Recognizer.
  • Stanford Word Segmenter – Tokenization of raw text is a standard pre-processing step for many NLP tasks.
  • Tregex, Tsurgeon and Semgrex – Tregex is a utility for matching patterns in trees, based on tree relationships and regular expression matches on nodes (the name is short for “tree regular expressions”).
  • Stanford Phrasal: A Phrase-Based Translation System
  • Stanford English Tokenizer – Stanford Phrasal is a state-of-the-art statistical phrase-based machine translation system, written in Java.
  • Stanford Tokens Regex – A tokenizer divides text into a sequence of tokens, which roughly correspond to “words”
  • Stanford Temporal Tagger – SUTime is a library for recognizing and normalizing time expressions.
  • Stanford SPIED – Learning entities from unlabeled text starting with seed sets using patterns in an iterative fashion
  • Stanford Topic Modeling Toolbox – Topic modeling tools to social scientists and others who wish to perform analysis on datasets
  • Twitter Text Java – A Java implementation of Twitter’s text processing library
  • MALLET – A Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
  • OpenNLP – a machine learning based toolkit for the processing of natural language text.
  • LingPipe – A tool kit for processing text using computational linguistics.
  • ClearTK – ClearTK provides a framework for developing statistical natural language processing (NLP) components in Java and is built on top of Apache UIMA.
  • Apache cTAKES – Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) is an open-source natural language processing system for information extraction from electronic medical record clinical free-text.
  • ClearNLP – The ClearNLP project provides software and resources for natural language processing. The project started at the Center for Computational Language and EducAtion Research, and is currently developed by the Center for Language and Information Research at Emory University. This project is under the Apache 2 license.
  • CogcompNLP – This project collects a number of core libraries for Natural Language Processing (NLP) developed in the University of Illinois’ Cognitive Computation Group, for example illinois-core-utilities which provides a set of NLP-friendly data structures and a number of NLP-related utilities that support writing NLP applications, running experiments, etc, illinois-edison a library for feature extraction from illinois-core-utilities data structures and many other packages.

General-Purpose Machine Learning

  • aerosolve – A machine learning library by Airbnb designed from the ground up to be human friendly.
  • Datumbox – Machine Learning framework for rapid development of Machine Learning and Statistical applications
  • ELKI – Java toolkit for data mining. (unsupervised: clustering, outlier detection etc.)
  • Encog – An advanced neural network and machine learning framework. Encog contains classes to create a wide variety of networks, as well as support classes to normalize and process data for these neural networks. Encog trains using multithreaded resilient propagation. Encog can also make use of a GPU to further speed processing time. A GUI based workbench is also provided to help model and train neural networks.
  • FlinkML in Apache Flink – Distributed machine learning library in Flink
  • H2O – ML engine that supports distributed learning on Hadoop, Spark or your laptop via APIs in R, Python, Scala, REST/JSON.
  • htm.java – General Machine Learning library using Numenta’s Cortical Learning Algorithm
  • java-deeplearning – Distributed Deep Learning Platform for Java, Clojure,Scala
  • Mahout – Distributed machine learning
  • Meka – An open source implementation of methods for multi-label classification and evaluation (extension to Weka).
  • MLlib in Apache Spark – Distributed machine learning library in Spark
  • Neuroph – Neuroph is lightweight Java neural network framework
  • ORYX – Lambda Architecture Framework using Apache Spark and Apache Kafka with a specialization for real-time large-scale machine learning.
  • Samoa SAMOA is a framework that includes distributed machine learning for data streams with an interface to plug-in different stream processing platforms.
  • RankLib – RankLib is a library of learning to rank algorithms
  • rapaio – statistics, data mining and machine learning toolbox in Java
  • RapidMiner – RapidMiner integration into Java code
  • Stanford Classifier – A classifier is a machine learning tool that will take data items and place them into one of k classes.
  • SmileMiner – Statistical Machine Intelligence & Learning Engine
  • SystemML – flexible, scalable machine learning (ML) language.
  • WalnutiQ – object oriented model of the human brain
  • Weka – Weka is a collection of machine learning algorithms for data mining tasks
  • LBJava – Learning Based Java is a modeling language for the rapid development of software systems, offers a convenient, declarative syntax for classifier and constraint definition directly in terms of the objects in the programmer’s application.

Speech Recognition

  • CMU Sphinx – Open Source Toolkit For Speech Recognition purely based on Java speech recognition library.

Data Analysis / Data Visualization

  • Flink – Open source platform for distributed stream and batch data processing.
  • Hadoop – Hadoop/HDFS
  • Spark – Spark is a fast and general engine for large-scale data processing.
  • Storm – Storm is a distributed realtime computation system.
  • Impala – Real-time Query for Hadoop
  • DataMelt – Mathematics software for numeric computation, statistics, symbolic calculations, data analysis and data visualization.
  • Dr. Michael Thomas Flanagan’s Java Scientific Library

Deep Learning

  • Deeplearning4j – Scalable deep learning for industry with parallel GPUs

Javascript

Natural Language Processing

  • Twitter-text – A JavaScript implementation of Twitter’s text processing library
  • NLP.js – NLP utilities in javascript and coffeescript
  • natural – General natural language facilities for node
  • Knwl.js – A Natural Language Processor in JS
  • Retext – Extensible system for analyzing and manipulating natural language
  • TextProcessing – Sentiment analysis, stemming and lemmatization, part-of-speech tagging and chunking, phrase extraction and named entity recognition.
  • NLP Compromise – Natural Language processing in the browser

Data Analysis / Data Visualization

  • D3.js
  • High Charts
  • NVD3.js
  • dc.js
  • chartjs
  • dimple
  • amCharts
  • D3xter – Straight forward plotting built on D3
  • statkit – Statistics kit for JavaScript
  • datakit – A lightweight framework for data analysis in JavaScript
  • science.js – Scientific and statistical computing in JavaScript.
  • Z3d – Easily make interactive 3d plots built on Three.js
  • Sigma.js – JavaScript library dedicated to graph drawing.
  • C3.js– customizable library based on D3.js for easy chart drawing.
  • Datamaps– Customizable SVG map/geo visualizations using D3.js.
  • ZingChart– library written on Vanilla JS for big data visualization.
  • cheminfo – Platform for data visualization and analysis, using the visualizer project.

General-Purpose Machine Learning

  • Convnet.js – ConvNetJS is a Javascript library for training Deep Learning models[DEEP LEARNING]
  • Clusterfck – Agglomerative hierarchical clustering implemented in Javascript for Node.js and the browser
  • Clustering.js – Clustering algorithms implemented in Javascript for Node.js and the browser
  • Decision Trees – NodeJS Implementation of Decision Tree using ID3 Algorithm
  • DN2A – Digital Neural Networks Architecture
  • figue – K-means, fuzzy c-means and agglomerative clustering
  • Node-fann – FANN (Fast Artificial Neural Network Library) bindings for Node.js
  • Kmeans.js – Simple Javascript implementation of the k-means algorithm, for node.js and the browser
  • LDA.js – LDA topic modeling for node.js
  • Learning.js – Javascript implementation of logistic regression/c4.5 decision tree
  • Machine Learning – Machine learning library for Node.js
  • mil-tokyo – List of several machine learning libraries
  • Node-SVM – Support Vector Machine for nodejs
  • Brain – Neural networks in JavaScript [Deprecated]
  • Bayesian-Bandit – Bayesian bandit implementation for Node and the browser.
  • Synaptic – Architecture-free neural network library for node.js and the browser
  • kNear – JavaScript implementation of the k nearest neighbors algorithm for supervised learning
  • NeuralN – C++ Neural Network library for Node.js. It has advantage on large dataset and multi-threaded training.
  • kalman – Kalman filter for Javascript.
  • shaman – node.js library with support for both simple and multiple linear regression.
  • ml.js – Machine learning and numerical analysis tools for Node.js and the Browser!
  • Pavlov.js – Reinforcement learning using Markov Decision Processes
  • MXNet – Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.

Misc

  • sylvester – Vector and Matrix math for JavaScript.
  • simple-statistics – A JavaScript implementation of descriptive, regression, and inference statistics. Implemented in literate JavaScript with no dependencies, designed to work in all modern browsers (including IE) as well as in node.js.
  • regression-js – A javascript library containing a collection of least squares fitting methods for finding a trend in a set of data.
  • Lyric – Linear Regression library.
  • GreatCircle – Library for calculating great circle distance.

Julia

General-Purpose Machine Learning

  • MachineLearning – Julia Machine Learning library
  • MLBase – A set of functions to support the development of machine learning algorithms
  • PGM – A Julia framework for probabilistic graphical models.
  • DA – Julia package for Regularized Discriminant Analysis
  • Regression – Algorithms for regression analysis (e.g. linear regression and logistic regression)
  • Local Regression – Local regression, so smooooth!
  • Naive Bayes – Simple Naive Bayes implementation in Julia
  • Mixed Models – A Julia package for fitting (statistical) mixed-effects models
  • Simple MCMC – basic mcmc sampler implemented in Julia
  • Distance – Julia module for Distance evaluation
  • Decision Tree – Decision Tree Classifier and Regressor
  • Neural – A neural network in Julia
  • MCMC – MCMC tools for Julia
  • Mamba – Markov chain Monte Carlo (MCMC) for Bayesian analysis in Julia
  • GLM – Generalized linear models in Julia
  • Online Learning
  • GLMNet – Julia wrapper for fitting Lasso/ElasticNet GLM models using glmnet
  • Clustering – Basic functions for clustering data: k-means, dp-means, etc.
  • SVM – SVM’s for Julia
  • Kernal Density – Kernel density estimators for julia
  • Dimensionality Reduction – Methods for dimensionality reduction
  • NMF – A Julia package for non-negative matrix factorization
  • ANN – Julia artificial neural networks
  • Mocha – Deep Learning framework for Julia inspired by Caffe
  • XGBoost – eXtreme Gradient Boosting Package in Julia
  • ManifoldLearning – A Julia package for manifold learning and nonlinear dimensionality reduction
  • MXNet – Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.
  • Merlin – Flexible Deep Learning Framework in Julia
  • ROCAnalysis – Receiver Operating Characteristics and functions for evaluation probabilistic binary classifiers
  • GaussianMixtures – Large scale Gaussian Mixture Models
  • ScikitLearn – Julia implementation of the scikit-learn API

Natural Language Processing

Data Analysis / Data Visualization

  • Graph Layout – Graph layout algorithms in pure Julia
  • Data Frames Meta – Metaprogramming tools for DataFrames
  • Julia Data – library for working with tabular data in Julia
  • Data Read – Read files from Stata, SAS, and SPSS
  • Hypothesis Tests – Hypothesis tests for Julia
  • Gadfly – Crafty statistical graphics for Julia.
  • Stats – Statistical tests for Julia
  • RDataSets – Julia package for loading many of the data sets available in R
  • DataFrames – library for working with tabular data in Julia
  • Distributions – A Julia package for probability distributions and associated functions.
  • Data Arrays – Data structures that allow missing values
  • Time Series – Time series toolkit for Julia
  • Sampling – Basic sampling algorithms for Julia

Misc Stuff / Presentations

  • DSP – Digital Signal Processing (filtering, periodograms, spectrograms, window functions).
  • JuliaCon Presentations – Presentations for JuliaCon
  • SignalProcessing – Signal Processing tools for Julia
  • Images – An image library for Julia

Lua

General-Purpose Machine Learning

  • Torch7
    • cephes – Cephes mathematical functions library, wrapped for Torch. Provides and wraps the 180+ special mathematical functions from the Cephes mathematical library, developed by Stephen L. Moshier. It is used, among many other places, at the heart of SciPy.
    • autograd – Autograd automatically differentiates native Torch code. Inspired by the original Python version.
    • graph – Graph package for Torch
    • randomkit – Numpy’s randomkit, wrapped for Torch
    • signal – A signal processing toolbox for Torch-7. FFT, DCT, Hilbert, cepstrums, stft
    • nn – Neural Network package for Torch
    • torchnet – framework for torch which provides a set of abstractions aiming at encouraging code re-use as well as encouraging modular programming
    • nngraph – This package provides graphical computation for nn library in Torch7.
    • nnx – A completely unstable and experimental package that extends Torch’s builtin nn library
    • rnn – A Recurrent Neural Network library that extends Torch’s nn. RNNs, LSTMs, GRUs, BRNNs, BLSTMs, etc.
    • dpnn – Many useful features that aren’t part of the main nn package.
    • dp – A deep learning library designed for streamlining research and development using the Torch7 distribution. It emphasizes flexibility through the elegant use of object-oriented design patterns.
    • optim – An optimization library for Torch. SGD, Adagrad, Conjugate-Gradient, LBFGS, RProp and more.
    • unsup – A package for unsupervised learning in Torch. Provides modules that are compatible with nn (LinearPsd, ConvPsd, AutoEncoder, …), and self-contained algorithms (k-means, PCA).
    • manifold – A package to manipulate manifolds
    • svm – Torch-SVM library
    • lbfgs – FFI Wrapper for liblbfgs
    • vowpalwabbit – An old vowpalwabbit interface to torch.
    • OpenGM – OpenGM is a C++ library for graphical modeling, and inference. The Lua bindings provide a simple way of describing graphs, from Lua, and then optimizing them with OpenGM.
    • sphagetti – Spaghetti (sparse linear) module for torch7 by @MichaelMathieu
    • LuaSHKit – A lua wrapper around the Locality sensitive hashing library SHKit
    • kernel smoothing – KNN, kernel-weighted average, local linear regression smoothers
    • cutorch – Torch CUDA Implementation
    • cunn – Torch CUDA Neural Network Implementation
    • imgraph – An image/graph library for Torch. This package provides routines to construct graphs on images, segment them, build trees out of them, and convert them back to images.
    • videograph – A video/graph library for Torch. This package provides routines to construct graphs on videos, segment them, build trees out of them, and convert them back to videos.
    • saliency – code and tools around integral images. A library for finding interest points based on fast integral histograms.
    • stitch – allows us to use hugin to stitch images and apply same stitching to a video sequence
    • sfm – A bundle adjustment/structure from motion package
    • fex – A package for feature extraction in Torch. Provides SIFT and dSIFT modules.
    • OverFeat – A state-of-the-art generic dense feature extractor
  • Numeric Lua
  • Lunatic Python
  • SciLua
  • Lua – Numerical Algorithms
  • Lunum

Demos and Scripts

  • Core torch7 demos repository.
    • linear-regression, logistic-regression
    • face detector (training and detection as separate demos)
    • mst-based-segmenter
    • train-a-digit-classifier
    • train-autoencoder
    • optical flow demo
    • train-on-housenumbers
    • train-on-cifar
    • tracking with deep nets
    • kinect demo
    • filter-bank visualization
    • saliency-networks
  • Training a Convnet for the Galaxy-Zoo Kaggle challenge(CUDA demo)
  • Music Tagging – Music Tagging scripts for torch7
  • torch-datasets – Scripts to load several popular datasets including:
    • BSR 500
    • CIFAR-10
    • COIL
    • Street View House Numbers
    • MNIST
    • NORB
  • Atari2600 – Scripts to generate a dataset with static frames from the Arcade Learning Environment

Matlab

Computer Vision

  • Contourlets – MATLAB source code that implements the contourlet transform and its utility functions.
  • Shearlets – MATLAB code for shearlet transform
  • Curvelets – The Curvelet transform is a higher dimensional generalization of the Wavelet transform designed to represent images at different scales and different angles.
  • Bandlets – MATLAB code for bandlet transform
  • mexopencv – Collection and a development kit of MATLAB mex functions for OpenCV library

Natural Language Processing

  • NLP – An NLP library for Matlab

General-Purpose Machine Learning

Data Analysis / Data Visualization

  • matlab_gbl – MatlabBGL is a Matlab package for working with graphs.
  • gamic – Efficient pure-Matlab implementations of graph algorithms to complement MatlabBGL’s mex functions.

.NET

Computer Vision

  • OpenCVDotNet – A wrapper for the OpenCV project to be used with .NET applications.
  • Emgu CV – Cross platform wrapper of OpenCV which can be compiled in Mono to e run on Windows, Linus, Mac OS X, iOS, and Android.
  • AForge.NET – Open source C# framework for developers and researchers in the fields of Computer Vision and Artificial Intelligence. Development has now shifted to GitHub.
  • Accord.NET – Together with AForge.NET, this library can provide image processing and computer vision algorithms to Windows, Windows RT and Windows Phone. Some components are also available for Java and Android.

Natural Language Processing

  • Stanford.NLP for .NET – A full port of Stanford NLP packages to .NET and also available precompiled as a NuGet package.

General-Purpose Machine Learning

  • Accord-Framework -The Accord.NET Framework is a complete framework for building machine learning, computer vision, computer audition, signal processing and statistical applications.
  • Accord.MachineLearning – Support Vector Machines, Decision Trees, Naive Bayesian models, K-means, Gaussian Mixture models and general algorithms such as Ransac, Cross-validation and Grid-Search for machine-learning applications. This package is part of the Accord.NET Framework.
  • DiffSharp – An automatic differentiation (AD) library providing exact and efficient derivatives (gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products) for machine learning and optimization applications. Operations can be nested to any level, meaning that you can compute exact higher-order derivatives and differentiate functions that are internally making use of differentiation, for applications such as hyperparameter optimization.
  • Vulpes – Deep belief and deep learning implementation written in F# and leverages CUDA GPU execution with Alea.cuBase.
  • Encog – An advanced neural network and machine learning framework. Encog contains classes to create a wide variety of networks, as well as support classes to normalize and process data for these neural networks. Encog trains using multithreaded resilient propagation. Encog can also make use of a GPU to further speed processing time. A GUI based workbench is also provided to help model and train neural networks.
  • Neural Network Designer – DBMS management system and designer for neural networks. The designer application is developed using WPF, and is a user interface which allows you to design your neural network, query the network, create and configure chat bots that are capable of asking questions and learning from your feed back. The chat bots can even scrape the internet for information to return in their output as well as to use for learning.

Data Analysis / Data Visualization

  • numl – numl is a machine learning library intended to ease the use of using standard modeling techniques for both prediction and clustering.
  • Math.NET Numerics – Numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Supports .Net 4.0, .Net 3.5 and Mono on Windows, Linux and Mac; Silverlight 5, WindowsPhone/SL 8, WindowsPhone 8.1 and Windows 8 with PCL Portable Profiles 47 and 344; Android/iOS with Xamarin.
  • Sho – Sho is an interactive environment for data analysis and scientific computing that lets you seamlessly connect scripts (in IronPython) with compiled code (in .NET) to enable fast and flexible prototyping. The environment includes powerful and efficient libraries for linear algebra as well as data visualization that can be used from any .NET language, as well as a feature-rich interactive shell for rapid development.

Objective C

General-Purpose Machine Learning

  • YCML – A Machine Learning framework for Objective-C and Swift (OS X / iOS).
  • MLPNeuralNet – Fast multilayer perceptron neural network library for iOS and Mac OS X. MLPNeuralNet predicts new examples by trained neural network. It is built on top of the Apple’s Accelerate Framework, using vectorized operations and hardware acceleration if available.
  • MAChineLearning – An Objective-C multilayer perceptron library, with full support for training through backpropagation. Implemented using vDSP and vecLib, it’s 20 times faster than its Java equivalent. Includes sample code for use from Swift.
  • BPN-NeuralNetwork – It implemented 3 layers neural network ( Input Layer, Hidden Layer and Output Layer ) and it named Back Propagation Neural Network (BPN). This network can be used in products recommendation, user behavior analysis, data mining and data analysis.
  • Multi-Perceptron-NeuralNetwork – it implemented multi-perceptrons neural network (ニューラルネットワーク) based on Back Propagation Neural Network (BPN) and designed unlimited-hidden-layers.
  • KRHebbian-Algorithm – It is a non-supervisor and self-learning algorithm (adjust the weights) in neural network of Machine Learning.
  • KRKmeans-Algorithm – It implemented K-Means the clustering and classification algorithm. It could be used in data mining and image compression.
  • KRFuzzyCMeans-Algorithm – It implemented Fuzzy C-Means (FCM) the fuzzy clustering / classification algorithm on Machine Learning. It could be used in data mining and image compression.

OCaml

General-Purpose Machine Learning

  • Oml – A general statistics and machine learning library.
  • GPR – Efficient Gaussian Process Regression in OCaml.
  • Libra-Tk – Algorithms for learning and inference with discrete probabilistic models.

PHP

Natural Language Processing

  • jieba-php – Chinese Words Segmentation Utilities.

General-Purpose Machine Learning

  • PredictionBuilder – A library for machine learning that builds predictions using a linear regression.

Python

Computer Vision

  • Scikit-Image – A collection of algorithms for image processing in Python.
  • SimpleCV – An open source computer vision framework that gives access to several high-powered computer vision libraries, such as OpenCV. Written on Python and runs on Mac, Windows, and Ubuntu Linux.
  • Vigranumpy – Python bindings for the VIGRA C++ computer vision library.
  • OpenFace – Free and open source face recognition with deep neural networks.
  • PCV – Open source Python module for computer vision

Natural Language Processing

  • NLTK – A leading platform for building Python programs to work with human language data.
  • Pattern – A web mining module for the Python programming language. It has tools for natural language processing, machine learning, among others.
  • Quepy – A python framework to transform natural language questions to queries in a database query language
  • TextBlob – Providing a consistent API for diving into common natural language processing (NLP) tasks. Stands on the giant shoulders of NLTK and Pattern, and plays nicely with both.
  • YAlign – A sentence aligner, a friendly tool for extracting parallel sentences from comparable corpora.
  • jieba – Chinese Words Segmentation Utilities.
  • SnowNLP – A library for processing Chinese text.
  • spammy – A library for email Spam filtering built on top of nltk
  • loso – Another Chinese segmentation library.
  • genius – A Chinese segment base on Conditional Random Field.
  • KoNLPy – A Python package for Korean natural language processing.
  • nut – Natural language Understanding Toolkit
  • Rosetta – Text processing tools and wrappers (e.g. Vowpal Wabbit)
  • BLLIP Parser – Python bindings for the BLLIP Natural Language Parser (also known as the Charniak-Johnson parser)
  • PyNLPl – Python Natural Language Processing Library. General purpose NLP library for Python. Also contains some specific modules for parsing common NLP formats, most notably for FoLiA, but also ARPA language models, Moses phrasetables, GIZA++ alignments.
  • python-ucto – Python binding to ucto (a unicode-aware rule-based tokenizer for various languages)
  • python-frog – Python binding to Frog, an NLP suite for Dutch. (pos tagging, lemmatisation, dependency parsing, NER)
  • python-zpar – Python bindings for ZPar, a statistical part-of-speech-tagger, constiuency parser, and dependency parser for English.
  • colibri-core – Python binding to C++ library for extracting and working with with basic linguistic constructions such as n-grams and skipgrams in a quick and memory-efficient way.
  • spaCy – Industrial strength NLP with Python and Cython.
  • PyStanfordDependencies – Python interface for converting Penn Treebank trees to Stanford Dependencies.
  • Distance – Levenshtein and Hamming distance computation
  • Fuzzy Wuzzy – Fuzzy String Matching in Python
  • jellyfish – a python library for doing approximate and phonetic matching of strings.
  • editdistance – fast implementation of edit distance
  • textacy – higher-level NLP built on Spacy

General-Purpose Machine Learning

  • machine learning – automated build consisting of a web-interface, and set of programmatic-interface API, for support vector machines. Corresponding dataset(s) are stored into a SQL database, then generated model(s) used for prediction(s), are stored into a NoSQL datastore.
  • XGBoost – Python bindings for eXtreme Gradient Boosting (Tree) Library
  • Bayesian Methods for Hackers – Book/iPython notebooks on Probabilistic Programming in Python
  • Featureforge A set of tools for creating and testing machine learning features, with a scikit-learn compatible API
  • MLlib in Apache Spark – Distributed machine learning library in Spark
  • scikit-learn – A Python module for machine learning built on top of SciPy.
  • metric-learn – A Python module for metric learning.
  • SimpleAI Python implementation of many of the artificial intelligence algorithms described on the book “Artificial Intelligence, a Modern Approach”. It focuses on providing an easy to use, well documented and tested library.
  • astroML – Machine Learning and Data Mining for Astronomy.
  • graphlab-create – A library with various machine learning models (regression, clustering, recommender systems, graph analytics, etc.) implemented on top of a disk-backed DataFrame.
  • BigML – A library that contacts external servers.
  • pattern – Web mining module for Python.
  • NuPIC – Numenta Platform for Intelligent Computing.
  • Pylearn2 – A Machine Learning library based on Theano.
  • keras – Modular neural network library based on Theano.
  • Lasagne – Lightweight library to build and train neural networks in Theano.
  • hebel – GPU-Accelerated Deep Learning Library in Python.
  • Chainer – Flexible neural network framework
  • gensim – Topic Modelling for Humans.
  • topik – Topic modelling toolkit
  • PyBrain – Another Python Machine Learning Library.
  • Brainstorm – Fast, flexible and fun neural networks. This is the successor of PyBrain.
  • Crab – A flexible, fast recommender engine.
  • python-recsys – A Python library for implementing a Recommender System.
  • thinking bayes – Book on Bayesian Analysis
  • Restricted Boltzmann Machines -Restricted Boltzmann Machines in Python. [DEEP LEARNING]
  • Bolt – Bolt Online Learning Toolbox
  • CoverTree – Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree
  • nilearn – Machine learning for NeuroImaging in Python
  • imbalanced-learn – Python module to perform under sampling and over sampling with various techniques.
  • Shogun – The Shogun Machine Learning Toolbox
  • Pyevolve – Genetic algorithm framework.
  • Caffe – A deep learning framework developed with cleanliness, readability, and speed in mind.
  • breze – Theano based library for deep and recurrent neural networks
  • pyhsmm – library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.
  • mrjob – A library to let Python program run on Hadoop.
  • SKLL – A wrapper around scikit-learn that makes it simpler to conduct experiments.
  • neurolabhttps://github.com/zueve/neurolab
  • Spearmint – Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Larochelle and Ryan P. Adams. Advances in Neural Information Processing Systems, 2012.
  • Pebl – Python Environment for Bayesian Learning
  • Theano – Optimizing GPU-meta-programming code generating array oriented optimizing math compiler in Python
  • TensorFlow – Open source software library for numerical computation using data flow graphs
  • yahmm – Hidden Markov Models for Python, implemented in Cython for speed and efficiency.
  • python-timbl – A Python extension module wrapping the full TiMBL C++ programming interface. Timbl is an elaborate k-Nearest Neighbours machine learning toolkit.
  • deap – Evolutionary algorithm framework.
  • pydeep – Deep Learning In Python
  • mlxtend – A library consisting of useful tools for data science and machine learning tasks.
  • neon – Nervana’s high-performance Python-based Deep Learning framework [DEEP LEARNING]
  • Optunity – A library dedicated to automated hyperparameter optimization with a simple, lightweight API to facilitate drop-in replacement of grid search.
  • Neural Networks and Deep Learning – Code samples for my book “Neural Networks and Deep Learning” [DEEP LEARNING]
  • Annoy – Approximate nearest neighbours implementation
  • skflow – Simplified interface for TensorFlow, mimicking Scikit Learn.
  • TPOT – Tool that automatically creates and optimizes machine learning pipelines using genetic programming. Consider it your personal data science assistant, automating a tedious part of machine learning.
  • pgmpy A python library for working with Probabilistic Graphical Models.
  • DIGITS – The Deep Learning GPU Training System (DIGITS) is a web application for training deep learning models.
  • Orange – Open source data visualization and data analysis for novices and experts.
  • MXNet – Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.
  • milk – Machine learning toolkit focused on supervised classification.
  • TFLearn – Deep learning library featuring a higher-level API for TensorFlow.
  • REP – an IPython-based environment for conducting data-driven research in a consistent and reproducible way. REP is not trying to substitute scikit-learn, but extends it and provides better user experience.

Data Analysis / Data Visualization

  • SciPy – A Python-based ecosystem of open-source software for mathematics, science, and engineering.
  • NumPy – A fundamental package for scientific computing with Python.
  • Numba – Python JIT (just in time) complier to LLVM aimed at scientific Python by the developers of Cython and NumPy.
  • NetworkX – A high-productivity software for complex networks.
  • igraph – binding to igraph library – General purpose graph library
  • Pandas – A library providing high-performance, easy-to-use data structures and data analysis tools.
  • Open Mining – Business Intelligence (BI) in Python (Pandas web interface)
  • PyMC – Markov Chain Monte Carlo sampling toolkit.
  • zipline – A Pythonic algorithmic trading library.
  • PyDy – Short for Python Dynamics, used to assist with workflow in the modeling of dynamic motion based around NumPy, SciPy, IPython, and matplotlib.
  • SymPy – A Python library for symbolic mathematics.
  • statsmodels – Statistical modeling and econometrics in Python.
  • astropy – A community Python library for Astronomy.
  • matplotlib – A Python 2D plotting library.
  • bokeh – Interactive Web Plotting for Python.
  • plotly – Collaborative web plotting for Python and matplotlib.
  • vincent – A Python to Vega translator.
  • d3py – A plotting library for Python, based on D3.js.
  • ggplot – Same API as ggplot2 for R.
  • ggfortify – Unified interface to ggplot2 popular R packages.
  • Kartograph.py – Rendering beautiful SVG maps in Python.
  • pygal – A Python SVG Charts Creator.
  • PyQtGraph – A pure-python graphics and GUI library built on PyQt4 / PySide and NumPy.
  • pycascading
  • Petrel – Tools for writing, submitting, debugging, and monitoring Storm topologies in pure Python.
  • Blaze – NumPy and Pandas interface to Big Data.
  • emcee – The Python ensemble sampling toolkit for affine-invariant MCMC.
  • windML – A Python Framework for Wind Energy Analysis and Prediction
  • vispy – GPU-based high-performance interactive OpenGL 2D/3D data visualization library
  • cerebro2 A web-based visualization and debugging platform for NuPIC.
  • NuPIC Studio An all-in-one NuPIC Hierarchical Temporal Memory visualization and debugging super-tool!
  • SparklingPandas Pandas on PySpark (POPS)
  • Seaborn – A python visualization library based on matplotlib
  • bqplot – An API for plotting in Jupyter (IPython)
  • pastalog – Simple, realtime visualization of neural network training performance.
  • caravel – A data exploration platform designed to be visual, intuitive, and interactive.
  • Dora – Tools for exploratory data analysis in Python.
  • Ruffus – Computation Pipeline library for python.
  • SOMPY – Self Organizing Map written in Python (Uses neural networks for data analysis).
  • HDBScan – implementation of the hdbscan algorithm in Python – used for clustering

Misc Scripts / iPython Notebooks / Codebases

Neural networks

  • Neural networks – NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.

Kaggle Competition Source Code

Ruby

Natural Language Processing

  • Treat – Text REtrieval and Annotation Toolkit, definitely the most comprehensive toolkit I’ve encountered so far for Ruby
  • Ruby Linguistics – Linguistics is a framework for building linguistic utilities for Ruby objects in any language. It includes a generic language-independent front end, a module for mapping language codes into language names, and a module which contains various English-language utilities.
  • Stemmer – Expose libstemmer_c to Ruby
  • Ruby Wordnet – This library is a Ruby interface to WordNet
  • Raspel – raspell is an interface binding for ruby
  • UEA Stemmer – Ruby port of UEALite Stemmer – a conservative stemmer for search and indexing
  • Twitter-text-rb – A library that does auto linking and extraction of usernames, lists and hashtags in tweets

General-Purpose Machine Learning

Data Analysis / Data Visualization

  • rsruby – Ruby – R bridge
  • data-visualization-ruby – Source code and supporting content for my Ruby Manor presentation on Data Visualisation with Ruby
  • ruby-plot – gnuplot wrapper for ruby, especially for plotting roc curves into svg files
  • plot-rb – A plotting library in Ruby built on top of Vega and D3.
  • scruffy – A beautiful graphing toolkit for Ruby
  • SciRuby
  • Glean – A data management tool for humans
  • Bioruby
  • Arel

Misc

Rust

General-Purpose Machine Learning

  • deeplearn-rs – deeplearn-rs provides simple networks that use matrix multiplication, addition, and ReLU under the MIT license.
  • rustlearn – a machine learning framework featuring logistic regression, support vector machines, decision trees and random forests.
  • rusty-machine – a pure-rust machine learning library.
  • leaf – open source framework for machine intelligence, sharing concepts from TensorFlow and Caffe. Available under the MIT license. [Deprecated]
  • RustNN – RustNN is a feedforward neural network library.

R

General-Purpose Machine Learning

  • ahaz – ahaz: Regularization for semiparametric additive hazards regression
  • arules – arules: Mining Association Rules and Frequent Itemsets
  • bigrf – bigrf: Big Random Forests: Classification and Regression Forests for Large Data Sets
  • bigRR – bigRR: Generalized Ridge Regression (with special advantage for p >> n cases)
  • bmrm – bmrm: Bundle Methods for Regularized Risk Minimization Package
  • Boruta – Boruta: A wrapper algorithm for all-relevant feature selection
  • bst – bst: Gradient Boosting
  • C50 – C50: C5.0 Decision Trees and Rule-Based Models
  • caret – Classification and Regression Training: Unified interface to ~150 ML algorithms in R.
  • caretEnsemble – caretEnsemble: Framework for fitting multiple caret models as well as creating ensembles of such models.
  • Clever Algorithms For Machine Learning
  • CORElearn – CORElearn: Classification, regression, feature evaluation and ordinal evaluation
  • CoxBoost – CoxBoost: Cox models by likelihood based boosting for a single survival endpoint or competing risks
  • Cubist – Cubist: Rule- and Instance-Based Regression Modeling
  • e1071 – e1071: Misc Functions of the Department of Statistics (e1071), TU Wien
  • earth – earth: Multivariate Adaptive Regression Spline Models
  • elasticnet – elasticnet: Elastic-Net for Sparse Estimation and Sparse PCA
  • ElemStatLearn – ElemStatLearn: Data sets, functions and examples from the book: “The Elements of Statistical Learning, Data Mining, Inference, and Prediction” by Trevor Hastie, Robert Tibshirani and Jerome Friedman Prediction” by Trevor Hastie, Robert Tibshirani and Jerome Friedman
  • evtree – evtree: Evolutionary Learning of Globally Optimal Trees
  • forecast – forecast: Timeseries forecasting using ARIMA, ETS, STLM, TBATS, and neural network models
  • forecastHybrid – forecastHybrid: Automatic ensemble and cross validation of ARIMA, ETS, STLM, TBATS, and neural network models from the “forecast” package
  • fpc – fpc: Flexible procedures for clustering
  • frbs – frbs: Fuzzy Rule-based Systems for Classification and Regression Tasks
  • GAMBoost – GAMBoost: Generalized linear and additive models by likelihood based boosting
  • gamboostLSS – gamboostLSS: Boosting Methods for GAMLSS
  • gbm – gbm: Generalized Boosted Regression Models
  • glmnet – glmnet: Lasso and elastic-net regularized generalized linear models
  • glmpath – glmpath: L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model
  • GMMBoost – GMMBoost: Likelihood-based Boosting for Generalized mixed models
  • grplasso – grplasso: Fitting user specified models with Group Lasso penalty
  • grpreg – grpreg: Regularization paths for regression models with grouped covariates
  • h2o – A framework for fast, parallel, and distributed machine learning algorithms at scale — Deeplearning, Random forests, GBM, KMeans, PCA, GLM
  • hda – hda: Heteroscedastic Discriminant Analysis
  • Introduction to Statistical Learning
  • ipred – ipred: Improved Predictors
  • kernlab – kernlab: Kernel-based Machine Learning Lab
  • klaR – klaR: Classification and visualization
  • lars – lars: Least Angle Regression, Lasso and Forward Stagewise
  • lasso2 – lasso2: L1 constrained estimation aka ‘lasso’
  • LiblineaR – LiblineaR: Linear Predictive Models Based On The Liblinear C/C++ Library
  • LogicReg – LogicReg: Logic Regression
  • Machine Learning For Hackers
  • maptree – maptree: Mapping, pruning, and graphing tree models
  • mboost – mboost: Model-Based Boosting
  • medley – medley: Blending regression models, using a greedy stepwise approach
  • mlr – mlr: Machine Learning in R
  • mvpart – mvpart: Multivariate partitioning
  • ncvreg – ncvreg: Regularization paths for SCAD- and MCP-penalized regression models
  • nnet – nnet: Feed-forward Neural Networks and Multinomial Log-Linear Models
  • oblique.tree – oblique.tree: Oblique Trees for Classification Data
  • pamr – pamr: Pam: prediction analysis for microarrays
  • party – party: A Laboratory for Recursive Partytioning
  • partykit – partykit: A Toolkit for Recursive Partytioning
  • penalized – penalized: L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model
  • penalizedLDA – penalizedLDA: Penalized classification using Fisher’s linear discriminant
  • penalizedSVM – penalizedSVM: Feature Selection SVM using penalty functions
  • quantregForest – quantregForest: Quantile Regression Forests
  • randomForest – randomForest: Breiman and Cutler’s random forests for classification and regression
  • randomForestSRC – randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC)
  • rattle – rattle: Graphical user interface for data mining in R
  • rda – rda: Shrunken Centroids Regularized Discriminant Analysis
  • rdetools – rdetools: Relevant Dimension Estimation (RDE) in Feature Spaces
  • REEMtree – REEMtree: Regression Trees with Random Effects for Longitudinal (Panel) Data
  • relaxo – relaxo: Relaxed Lasso
  • rgenoud – rgenoud: R version of GENetic Optimization Using Derivatives
  • rgp – rgp: R genetic programming framework
  • Rmalschains – Rmalschains: Continuous Optimization using Memetic Algorithms with Local Search Chains (MA-LS-Chains) in R
  • rminer – rminer: Simpler use of data mining methods (e.g. NN and SVM) in classification and regression
  • ROCR – ROCR: Visualizing the performance of scoring classifiers
  • RoughSets – RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories
  • rpart – rpart: Recursive Partitioning and Regression Trees
  • RPMM – RPMM: Recursively Partitioned Mixture Model
  • RSNNS – RSNNS: Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS)
  • RWeka – RWeka: R/Weka interface
  • RXshrink – RXshrink: Maximum Likelihood Shrinkage via Generalized Ridge or Least Angle Regression
  • sda – sda: Shrinkage Discriminant Analysis and CAT Score Variable Selection
  • SDDA – SDDA: Stepwise Diagonal Discriminant Analysis
  • SuperLearner and subsemble – Multi-algorithm ensemble learning packages.
  • svmpath – svmpath: svmpath: the SVM Path algorithm
  • tgp – tgp: Bayesian treed Gaussian process models
  • tree – tree: Classification and regression trees
  • varSelRF – varSelRF: Variable selection using random forests
  • XGBoost.R – R binding for eXtreme Gradient Boosting (Tree) Library
  • Optunity – A library dedicated to automated hyperparameter optimization with a simple, lightweight API to facilitate drop-in replacement of grid search. Optunity is written in Python but interfaces seamlessly to R.
  • igraph – binding to igraph library – General purpose graph library
  • MXNet – Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.

Data Analysis / Data Visualization

  • ggplot2 – A data visualization package based on the grammar of graphics.

SAS

General-Purpose Machine Learning

  • Enterprise Miner – Data mining and machine learning that creates deployable models using a GUI or code.
  • Factory Miner – Automatically creates deployable machine learning models across numerous market or customer segments using a GUI.

Data Analysis / Data Visualization

  • SAS/STAT – For conducting advanced statistical analysis.
  • University Edition – FREE! Includes all SAS packages necessary for data analysis and visualization, and includes online SAS courses.

High Performance Machine Learning

Natural Language Processing

Demos and Scripts

  • ML_Tables – Concise cheat sheets containing machine learning best practices.
  • enlighten-apply – Example code and materials that illustrate applications of SAS machine learning techniques.
  • enlighten-integration – Example code and materials that illustrate techniques for integrating SAS with other analytics technologies in Java, PMML, Python and R.
  • enlighten-deep – Example code and materials that illustrate using neural networks with several hidden layers in SAS.
  • dm-flow – Library of SAS Enterprise Miner process flow diagrams to help you learn by example about specific data mining topics.

Scala

Natural Language Processing

  • ScalaNLP – ScalaNLP is a suite of machine learning and numerical computing libraries.
  • Breeze – Breeze is a numerical processing library for Scala.
  • Chalk – Chalk is a natural language processing library.
  • FACTORIE – FACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference.

Data Analysis / Data Visualization

  • MLlib in Apache Spark – Distributed machine learning library in Spark
  • Scalding – A Scala API for Cascading
  • Summing Bird – Streaming MapReduce with Scalding and Storm
  • Algebird – Abstract Algebra for Scala
  • xerial – Data management utilities for Scala
  • simmer – Reduce your data. A unix filter for algebird-powered aggregation.
  • PredictionIO – PredictionIO, a machine learning server for software developers and data engineers.
  • BIDMat – CPU and GPU-accelerated matrix library intended to support large-scale exploratory data analysis.
  • Wolfe Declarative Machine Learning
  • Flink – Open source platform for distributed stream and batch data processing.
  • Spark Notebook – Interactive and Reactive Data Science using Scala and Spark.

General-Purpose Machine Learning

  • Conjecture – Scalable Machine Learning in Scalding
  • brushfire – Distributed decision tree ensemble learning in Scala
  • ganitha – scalding powered machine learning
  • adam – A genomics processing engine and specialized file format built using Apache Avro, Apache Spark and Parquet. Apache 2 licensed.
  • bioscala – Bioinformatics for the Scala programming language
  • BIDMach – CPU and GPU-accelerated Machine Learning Library.
  • Figaro – a Scala library for constructing probabilistic models.
  • H2O Sparkling Water – H2O and Spark interoperability.
  • FlinkML in Apache Flink – Distributed machine learning library in Flink
  • DynaML – Scala Library/REPL for Machine Learning Research
  • Saul – Flexible Declarative Learning-Based Programming.

Swift

General-Purpose Machine Learning

  • Swift AI – Highly optimized artificial intelligence and machine learning library written in Swift.
  • BrainCore – The iOS and OS X neural network framework
  • swix – A bare bones library that includes a general matrix language and wraps some OpenCV for iOS development.
  • DeepLearningKit an Open Source Deep Learning Framework for Apple’s iOS, OS X and tvOS. It currently allows using deep convolutional neural network models trained in Caffe on Apple operating systems.
  • AIToolbox – A toolbox framework of AI modules written in Swift: Graphs/Trees, Linear Regression, Support Vector Machines, Neural Networks, PCA, KMeans, Genetic Algorithms, MDP, Mixture of Gaussians.
  • MLKit – A simple Machine Learning Framework written in Swift. Currently features Simple Linear Regression, Polynomial Regression, and Ridge Regression.

TensorFlow

General-Purpose Machine Learning

Credits