10 Must-Try Open Source Tools for Machine Learning

10 Must-Try Open Source Tools for Machine Learning

Tools to make ML easier

Making Machine Learning easier is more possible than you think. Today we have a great variety of free open-source tools for any tastes and demands. Exploiting these tools will definitely help you to manage complex tasks and acquire new skills faster. Besides, you can always visit various online communities connected with a certain tool, chat about the working process and reach lots of insights, and what could be better?

In this post, I will showcase the list of the best tools on the market now. For every instrument, I will give a brief description and highlight all the important details. After reading the article, you will be able to differentiate the things you need and thereby save your time and make your work more effective.

Who’s this article for?

  • For those who already know a little theory and want to use the knowledge in practice
  • For those who are still in search of a good tool for work
  • For those who are tired of mainstream tools like Microsoft Azure or Apache

Intriguing, isn’t it? If so, don’t hesitate. Just grab your coffee, comfortable chair, and dive in.

#1 Magenta

Well, yes, the first tool in this list is not dedicated to general ML tasks. Nevertheless, Magenta is totally worth your attention, cause it can help you to do a whole slew of creative stuff. It is a research project that demonstrates the potential of machine learning in creating works of art and music. For the most part, it is focused on developing new deep learning and reinforcement learning algorithms for synthesizing songs, images, or sketches.

What do I like about this tool?

  • The main repository is available to work in Python, but there is also a version of magenta.js for Javascript.
  • The biggest goal of Magenta is to make music and art using AI, so this is great tool for artists and musicians to extend their creative possibilities.

Magenta: Music and Art Generation with Machine Intelligence

GitHub

#2 Accord.NET

In comparing with previous tool, Accord with any exaggeration can be a cure for all treatments. It is a .Net machine learning framework combined with audio and image processing libraries written in C#. It includes the Accord.Math, Accord.Statistics, and Accord.MachineLearning. So, this is good both for creative and general tasks.

You can use its visual image processing algorithms for tasks such as face recognition, image joining, or tracking moving objects. Accord also includes libraries that provide a more traditional range of machine learning functions starting from neural networks and ending with decision tree systems.

What do I like about this tool?

  • You can use this tool for every task you have (computer audition, signal processing, statistics applications and more).
  • Consists of more than 40 parametric and non-parametric estimation of statistical distributions.
  • It contains more than 35 hypothesis tests, including one way and two-way ANOVA tests, non-parametric tests like Kolmogorov-Smirnov test and many more.
  • It has more than 38 kernel functions.

Accord / AForge.net Framework

Accord Framework on GitHub

#3 Knime

The next tool is also can be considered as classical solution for ML tasks. This framework allows you to implement a full cycle of data analysis including reading data from various sources, converting and filtering, actually analyzing, visualizing and exporting.

What do I like about this tool?

  • Visualization of the data analysis process provided free of charge
  • A good tool for those who want to analyze data and do not have programming skills
  • Also suitable for those who want to delve into a good library of implemented algorithms and, perhaps, learn something new
  • The interface is clear, you can easily create and produce the desired task

KNIME Deep Learning

GitHub

#4 Shogun

Shogun was created in 1999 and written in C ++, but it can be used with other languages like Java, Python, C #, Ruby, R, Lua, Octave, and Matlab. Shogun has a competitor. Another machine learning library, built on the basis of C ++, Mlpack, has been used only since 2011, but it is faster and easier to work with it thanks to an integrated set of APIs.

This tool is designed for large scale learning. Mainly, it focuses on kernel machines like support vector machines for classification and regression problem.

What do I like about this tool?

  • Allows linking to other AI and machine learning libraries like LibSVM, LibLinear, SVMLight, LibOCAS, etc.
  • It provides interfaces for Python, Lua, Octave, Java, C#, Ruby, MatLab, and R.
  • It can process a vast amount of data, like 10 million samples.
  • Great tool for practitioners has a wide range of standard and cutting-edge algorithms

Shogun: Unified and efficient machine learning library

GitHub

#5 Oryx 2

Oryx 2 is a realization of the lambda architecture built on Apache Spark and Apache Kafka, but with specialization for real-time large scale machine learning. It is a framework for building applications but also includes packaged, end-to-end applications for collaborative filtering, classification, regression, and clustering.

By the way, Oryx 2 is an upgraded version of the original Oryx 1 project. This platform is intended for developers of applications and vessels using some additional applications, as well as for end-to-end applications for filtering, classification, regression and clustering purposes.

What do I like about this tool?

  • It has three tiers: generic lambda architecture tier, specialization on top providing ML abstractions, end-to-end implementation of the same standard ML algorithms.
  • It consists of three side-by-side cooperating layers: batch layer, speed layer, serving layer.
  • There is also a data transport layer that moves data between layers and receives input from external sources.

Oryx 2

GitHub

#6 TensorFlow

Today Tensorflow is perhaps the most popular tool for every ML specialist. This tool implements data flow graphs when pieces of data (“tensors”) can be processed by a series of algorithms described by the graph. Moving data around the system is called “streams.” Graphs can be collected using C ++ or Python, and processed by a processor or video card.

TensorFlow contains a large amount of documentation, training materials, and online resources. Besides, Google has long-term plans for developing TensorFlow through third-party developers.

What do I like about this tool?

  • An end-to-end deep learning system
  • Build and train ML models effortlessly using intuitive high-level APIs like Keras with eager execution
  • This open-source software is highly flexible
  • Performs numerical computations using data flow graphs
  • Run-on CPUs or GPUs, and also on mobile computing platforms
  • Efficiently train and deploy the model in the cloud

TensorFlow

GitHub

#7 Eclipse Deeplearning4j

Eclipse Deeplearning4j is an open-source deep-learning library for Java Virtual Machine (JVM). Deeplearning4j is written in Java and compatible with any JVM language like Scala, Clojure or Kotlin. The goal of Eclipse Deeplearning4j is to provide a prominent set of components for developing the applications that integrate with Artificial Intelligence.

What do I like about this tool?

  • Allows configuring deep neural networks
  • Covers the entire deep learning workflow from data preprocessing to distributed training, hyperparameter optimization, and production-grade deployment
  • Provides a flexible integration for large enterprise environments
  • Utilized at the edge to support the Internet of Things (IoT) deployments

Eclipse Deeplearning4j

GitHub

#8 Scikit-learn

Python has become a popular programming language in mathematics and statistics due to the fact that it is easy to use and the fact that almost any application has the necessary libraries. Scikit makes the most of these features, extending to existing Python packages — NumPy, SciPy, and Matplotlib — for math. Such integrated libraries can be used for interactive applications in the development environment or be embedded in other software and reused. The kit is available under the BSD license, so it is fully open and reusable.

What do I like about this tool?

  • An efficient tool for data mining and data analysis tasks
  • It is built on NumPy, SciPy, and matplotlib
  • You can reuse this tool in various contexts.
  • Also, it is commercially useable beneath BSD license

scikit-learn: Machine Learning in Python

GitHub

#9 Veles

Another great tool for ML practitioners. Veles is a distributed platform for creating a deep learning application. Like TensorFlow and DMTK, it is written in C ++, although Python is used to automate and coordinate nodes. Before being fed to a cluster of data samples, they can be analyzed and automatically normalized. The REST API allows you to immediately use trained models in work projects (if you have powerful enough equipment).

Using Python in Veles goes beyond “sticky code.” For example, IPython (now Jupyter), a tool for visualizing and analyzing data, can output data from a Veles cluster. Samsung hopes open-source status will help drive further product development, as well as porting to Windows and Mac OS X.

What do I like about this tool?

  • Client Programming Languages: Python (IPython), Java(Mastodon), REST APIs enable access from other languages
  • Freat for Deep Learning
  • Though it is written in C++, It does use python to perform automation
  • It is mainly used in neural networks like CNN(convolution Neural Networks) recurrent neural networks

Veles

GitHub

#10 Nervana Neo

Last but not least. Nervana Neon — the fastest framework alive. It is the next generation of intelligent agents and applications. Nervana, together with Intel, is creating a software and hardware platform for deep learning. And as an open-source project, it offers the Neon framework. With the help of plug-ins, it can perform heavy computing on processors, graphics cards or equipment created by Nervana.

Neon is written in Python, with several pieces in C ++ and assembler. So if you are doing scientific work in Python, or using some other framework that has Python binders, you can immediately use Neon.

What do I like about this tool?

  • Founded in 2004, Neon provides developers with the ability to create, train, and deploy deep learning technologies in the clouds.
  • Neon offers a large number of video training materials and “zoo models” containing pre-training algorithms and sample scripts.

Nervana Neon

GitHub

Wrapping it up

I’m sure a lot of you would agree. If you haven’t yet embraced the power of open-source tools in machine learning — you’re missing out! I can undoubtedly recommend any of this tool for your daily usage. But the choice is yours.

Source: towardsdatascience