13 Open-Source Artificial Intelligence and Machine Learning Tools to Watch in 2019

Artificial Intelligence and Machine Learning are the buzzwords in the technology world today. AI has already influenced many aspects of our lives from the voice recognition system in our smartphones to driverless cars and robots that are performing human tasks.

Big companies like Google, Facebook, IBM, Microsoft, and Amazon are taking advantage of this rapidly growing future technology and investing in their own Artificial intelligence and Machine Learning Research and Development.

In this article, we are providing a list of 13 best open sources AI and Machine Learning tools that you can use to build your own upcoming projects.

Caffe

Caffe (Convolutional Architecture for Fast Feature Embedding) is an open-source artificial intelligence based deep learning framework developed by Berkeley AI Research (BAIR) center. This powerful framework is written in C++ programming language and comes with a Python interface.

This tool mainly focuses on expressiveness, speed, and modularity and it is a popular choice for computer vision related applications. Now Caffe is being extensively used in academic research projects, startup prototypes and even for large-scale industrial applications.

Features:

1. Its expressive design helps in quick processing of data and encourages application and innovation.
2. You can easily switch between CPU and GPU with its impressive looking architecture.
3. It is able to process over 60 million images per day with NVIDIA K40 GPU.
4. Its extensive code facilitates active development, fast performance for a huge number of different projects.
5. Speed is one of Caffe’s major factors that makes it perfect for research experiments and industrial applications.

TensorFlow

TensorFlow is an open source machine learning framework developed by researchers and engineers of Google. It is one of the most extensively used frameworks for performing numerical computation using data flow graphs. Its flexible architecture allows you to perform computation on multiple CPU or GPU using a single API.

TensorFlow is available on various programming languages such as C++, Java, Python, etc. and you can also find packages for other third-party programming languages.TensorFlow allows you to develop neural networks and it is now widely used by various reputed companies like Twitter, Dropbox, eBay, Intel, and many others.

Features:

1. With TensorFlow, you can easily check each and every part of the graph of a model and makes the necessary changes while debugging it.
2. TensorFlow is flexible in its operation that means it offers you the option of modularity and the parts of it you want to make standalone.
3. It lets you develop deep learning neural networks using flowgraphs that makes the models more efficient for large scale projects.
4. TensorFlow has feature columns that work between raw data and estimators to send or receive input data with your model.
5. With its LSTM (Long short-term memory) model you can automatically generate email responses.

Apache Mahout

Apache Mahout is an open source machine learning framework mainly used for creating scalable machine learning algorithms. This tool was designed by the Apache Software Foundation and it is built on a platform called Mahout Hadoop. It mainly focuses on simplifying common math problems involving statistics and linear algebra and it helps mathematicians, statisticians and data scientists to quickly implement their own algorithms.

It helps in processing and grouping big data in the Mahout Hadoop distributed file system. It has a cross-platform operating system and it gives you an environment with R-like syntax. Mahout is being utilized by organizations like Adobe, Accenture, Foursquare, LinkedIn, Twitter and others.

Features:

1. The algorithms are written on top of Hadoop, so it works well in a distributed environment.
2. Mahout offers you a ready to use framework for performing data mining tasks on large volumes of data.
3. It lets applications analyze large scale of data effectively in quick time.
4. It includes matrix and vector libraries.
5. It offers a wide variety of premade algorithms for tools like Spark, H2O, Apache Flink, etc.
6. It includes various Map-Reduce enabled clustering implementations such as k-means, fuzzy k-means, Canopy, Dirichlet, and Mean-Shift.
7. It supports Distributed Naive Bayes as well as Complementary Naive Bayes classification implementations.

Deeplearning4j

Deeplearning4j is another well-known open source AI based deep learning tool for Java Virtual Machine (JVM). It was developed under the Apache License 2.0 by a group of AI researchers of San Francisco and Tokyo.

Deeplearning4j can use different API languages like Python and Clojure and it integrates with both Hadoop and Apache Spark. This framework is used for building neural nets and it contains many advanced visualization tools. Deeplearning4j has many diverse academic applications for research-based projects and in the fields of cybersecurity, fraud detection, and image recognition.

Features:

1. It works with distributed CPUs and GPUs.
2. It can be used for multiple API languages including Java, Scala, Python, Clojure, and Kotlin.
3. It can import models from Python frameworks such as Keras, Tensorflow, Theano, and CNTK.
4. Deeplearning4j has integrated with other machine-learning platforms like RapidMiner, Prediction.io, and Weka.
5. Deeplearning4j allows to configure deep neural networks and it is compatible with Java, Scala and other Java Virtual Machine languages.

Apache SystemML

Apache SystemML is a flexible open-source artificial intelligence tool created by a group of researchers at IBM. SystemML focuses on big data and has been designed to simplify complex mathematical problems.

It runs on R-like and Python-like languages and it automatically scales your data line by line as well as determines whether your code should be run on the driver or an Apache Spark cluster. At present, this tool is being utilized by businesses like automotive or airport traffic control.

Features:

1. It comes with automatic optimization based on data and cluster characteristics that ensures both efficiency and scalability.
2. Apache SystemML has multiple execution modes like Spark MLContext, Spark Batch, Hadoop Batch, Standalone, and JMLC (Java Machine Learning Connector).
3. SystemML considered as SQL for machine learning and latest version (1.0.0) of SystemML supports Python 2.7/3.5+, Hadoop 2.6+, Java 8+, Scala 2.11+ and Spark 2.1+.

H20

H20 is an artificial intelligence based open-source deep learning platform designed by H2O.ai. This tool is used by developers and AI researchers that helps them to make decisions from data and draw insights. There are two open source versions available for H2O, one is standard H2O and the other one is paid version Sparkling Water.

Having two open source versions available this tool can be used for risk and fraud analysis, predictive modeling, advertising technology, insurance analytics, customer intelligence, and healthcare.

Features:

1. It supports various operating systems like Microsoft Windows, Linux, and MacOS.
2. Familiar and easy to use web UI and interfaces.
3. Real-time data scoring.
4. It can analyze data present in the cloud and Apache Hadoop file systems.
5. Comes with, the best of breed open source technology.
6. Hugely scalable big data analysis system.
7. It provides data diagnostic support for all common database and file types.

MLlib

MLlib is an open source machine learning library launched by Apache Spark for learning algorithms. This tool uses different programming languages like Scala, Java, R, Python and runs on many different platforms such as Kubernetes, Hadoop or Cloud.

This library contains many deep learning and core machine learning algorithms for classification, recommendation, decision trees, clustering, topic modeling, regression, model evaluation, feature transformations, ML persistence, ML pipeline construction, and survival analysis.

Features:

1. ML algorithms form the core of MLlib and it includes common learning algorithms such as clustering classification, regression, and collaborative filtering.
2. Pipelines offers tools for constructing, evaluating and tuning ML Pipelines.
3. Persistence helps you to save and load algorithms, models, and Pipelines.
4. Featurization includes feature extraction, dimensionality reduction, transformation, and selection.
5. Utilities provides support for linear algebra, statistics and data handling.

OpenCyc

OpenCyc is an open source general knowledge database launched by Cycorp that makes text understanding possible. OpenCyc has been designed to make sure that users have unrestricted access to the knowledge base and they can use it in different applications.

OpenCyc enables apps to go through a large database and process the relevant information to come up with an accurate result. It is compatible with Apache and its own built-in HTTP server.

Features:

1. It is helpful for rich domain modeling, text understanding, semantic data integration, domain-specific expert systems AI games and more.
2. This AI tool is valuable for conducting quiz contests, understanding text and learning knowledge within specific domains.
3. It helps in differentiating relative words and synonyms of a particular key search word and ensures the app performs like a person by showing human cognitive abilities.

OpenNN

OpenNN (Open Neural Networks) is another open-source class library written in C++ programming language for deep learning. It is used for creating neural networks, the main area of machine learning research.

This open source library is used in the logistics and marketing field and it allows high-performance computing as it has a higher processing speed.

Features:

1. OpenNN contains data mining algorithms for a number of functions.
2. OpenNN is based on the multilayer perceptron.
3. It uses machine learning techniques for solving data mining and predictive analytics tasks in different sectors such as energy, chemistry, and engineering.
4. To increase computer performance it allows multiprocessing programming by means of OpenMP.

Torch

Launched in 2002, Torch is a machine learning library that offers a wide range of algorithms for deep learning. It is written on fast scripting language LuaJIT, and an underlying C/CUDA implementation that makes it easy to use.

This open source framework provides you with optimized speed and flexibility when handling machine learning projects without causing complexities in the process. Torch is used by various popular companies like Facebook, IBM, Yandex, and the Idiap Research Institute. Now it has been extended for use on Android and iOS devices.

Features:

1. Comes with a powerful N-dimensional array.
2. Numeric optimization routines.
3. It consists of lots of routines for indexing, slicing, transposing, etc.
4. Neural network and energy-based models.
5. Fast and efficient GPU support.
6. Embeddable with ports to iOS and Android backends.

NuPIC

NuPIC (Numenta Platform for Intelligent Computing) is an open-source artificial intelligence project based on a theory called Hierarchical Temporal Memory (HTM). NuPIC was developed by Numenta, a machine intelligence company that has developed technology and applications based on the principles of the neocortex.

NuPIC is an AI learning framework that can be implemented in various programming languages like C++, Java, Python, Clojure, Go, or JavaScript. It gathers analytics from live data streams and it is a perfect tool for detecting inconsistency within live data.

Features:

1. Grok – Detects inconsistency for IT servers.
2. Cortical.io – This feature is used for advanced natural language processing.
3. HTM Studio – This tool finds irregularities in time series.
4. Numenta Anomaly Benchmark – It compares HTM with other irregularity detection techniques.

Distributed Machine Learning Toolkit

Distributed Machine Learning Toolkit is an open source project developed by Microsoft. This toolkit is designed to analyze big data applications and to run AI systems faster. It consists of three key components such as LightLDA topic model algorithm, DMTK framework, and the Distributed (Multisense) Word Embedding algorithm.

Microsoft’s Distributed Machine Learning Toolkit (DMTK) contains both algorithmic and system innovations. These innovations make machine learning tasks on big data highly scalable and efficient.

Features:

1. LightLDA is a very fast and scalable model algorithm with a O(1) Gibbs sampler and an efficient distributed implementation.
2. Distributed (Multi-sense) Word Embedding is a top quality word feature for natural language processing.
3. LightGBM is a very high-performance gradient boosting tree framework that supports different algorithms like GBDT, GBRT, GBM, and MART.

ONNX

ONNX (Open Neural Network Exchange) is an open source AI tool that is extensively used by AI researchers in deep learning models. It is primarily a Facebook open source project that is supported by Microsoft and AWS. With ONNX, AI developers can easily move models between state-of-the-art tools and choose the combination that is the best for them.

Features:

1. ONNX enables models to be trained in one framework and transferred to another for inference.
2. This tool allows you to apply innovative contents like extensible computation graph models and you can make changes in their networks according to the requirements.
3. Its compatible runtimes and libraries are designed to maximize the performance of some of the best hardware in the industry.

Conclusion:

The use of the above AI and Machine Learning tools is revolutionizing industries at a rapid pace. There is a vast scope to use these open source tools in internet marketing training, education & services, jobs & employment, medicine & healthcare, foods & environment, robotics, financial trading and many more. No doubt AI & ML offers you the most extraordinary possibilities to expand it worldwide and harness the untapped potentials of modern technologies and developments.