Best Open Source AI Tools for Ubuntu
Artificial Intelligence (AI) is currently one of the fastest-growing fields in science and technology and is being used to build software and hardware that can solve everyday challenges in industries like healthcare, education, banking, manufacturing, security, and many others.
So it goes without saying that Linux is a great platform for AI. Because Linux maintains compatibility with virtually any programming language, it can work seamlessly with a variety of platforms designed and developed for supporting AI. This is why many developers choose Linux-based platforms. And fortunately for budding developers there is a wealth of information online for them to get up to speed with the latest developments. Learning resources range from beginner classes on its business applications to advanced tutorials on deep reinforcement learning, with Udemy having more than a 100 AI courses on its online platform alone, to assist developers looking to hone their skillset.
To help you even more, we’ve compiled a list of five open-source AI tools for Ubuntu, which are guaranteed to take your AI programming to the next level.
Table of Contents
#1. Deep Learning for Java
Designed specifically for business, Eclipse Deep Learning for Java or Deeplearning4j (DL4J) is a commercial-grade open-source, distributed deep-learning library written for Java and Scala. It is designed to integrate with Hadoop and Spark on top of distributed GPUs and CPUs. Essentially DL4J allows you to compose deep neural nets from various shallow nets, or layers. While there are a lot of parameters that need to be set and a lot of knobs to turn when training a deep-learning network, DL4J can serve as a DIY tool for programmers using any JVM language like Java, Scala, Clojure or Kotlin.
#2. Tensor Flow
Tensor Flow is another open-source machine learning framework for Ubuntu. It was originally released in 2015, and designed to deploy across a variety of platforms. Originally developed by Google for research and production purposes, Tensor Flow allows researches to push the state of the art in machine learning (ML), and lets developers build and deploy ML-powered applications. The framework allows programmers to develop neural networks, as well as other computational models, using flowgraphs. Tensor Flow is also optimized to work with CUDA-Capable GPUs, so if you’re running an Nvidia chipset you can take advantage of the extra processing power.
Developed by H2O.ai, H2O aims to democratize AI and make ML easier for all. Their open-source community includes more than 129,000 data scientists and over 12,000 organizations. H2O is designed to allow users to draw insights from the data using faster and better predictive modeling. H2O code is Java-based and provides an open-source, distributed and scalable ML and predictive analytics platform. It’s faster than other platforms as data is distributed across the cluster and stored in memory in a compressed format. It supports smarter applications such a deep learning, random forests, logistic regression, gradient boosting and many more.
#4. Microsoft Cognitive Toolkit
The Microsoft Cognitive Toolkit is a commercial-grade distributed deep learning toolkit that can integrate with Linux-based platforms. It implements stochastic gradient descent learning with automatic differentiation and parallelization across a series of servers and GPUs. A relatively new addition to the Linux AI tool ecosystem, the open-source framework of Microsoft’s Cognitive Toolkit is capable of training deep learning algorithms to function like the human brain. Its features include optimized components capable of handling information and data from C++, Python or as a standalone ML tool through its own model description language, Brainscript.
NuPIC is an advanced open-source framework for ML based on the neocortex theory of Hierarchical Temporary Memory (HTM). HTM is a revolutionary foundational technology for the future of machine intelligence based on the biology of the human neocortex. Unlike other AI toolkits, HTM is not a deep learning or machine learning technology – it is a machine intelligence network based on neuroscience. NuPIC’s main purpose is to analyze real-time streaming data, where it can learn time-based patterns and predicts imminent values and reveals irregularities. Some of its features include temporal and spatial patterns, prediction and modeling, effective anomaly detection and hierarchical temporal memory.