10 Open Source and Free Tools for AI Developers

 

Introduction to AI and Machine Learning, Part 2 10 Open Source and Free Tools for AI Developers

The development of AI and machine learning systems is complex. Fortunately, there are a number of practical free tools that can assist software developers in their outstaffing work. We show which ones are.

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Some free and open source tools help you get started in the field of AI development.Some free and open source tools help you get started in the field of AI development.

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If you want to get involved in the development of machine learning and AI systems, you need a lot of practice and certain skills in the field of AI. The latter are in many ways a matter of personal characteristics.

Exercise, however, can be built up, and some free free platforms and tools for AI development can help. It is also about experimenting and not having to model AI software from scratch.

OpenAI Gym

The non-profit organization OpenAI is an initiative supported by Microsoft and Elon Musk to help develop artificial intelligence into a useful helper for humanity. Some great thinkers such as Stephen Hawking have, or indeed had, the legitimate fear that a self-improving AI could quickly prove to be a threat to humanity.

To counteract a dystopian scenario, OpenAI supports the research and development of helpful artificial intelligence. AI developers can benefit from this idea: In addition to the idea of providing as much AI technology as possible open source, the OpenAI Gym is a simple toolkit for developing simple learning algorithms.

In this way, developers can playfully familiarize themselves with the workings of deep learning algorithms. If you want more, you can also deal with the language processing of AI systems via the OpenAI API, a look at the beta channel of OpenAI is worthwhile in any case.

Google TensorFlow

Google also provides a free AI platform for developers: under the name TensorFlow, the search engine giant offers an open source library in Python for AI development. As an” end-to-end open-source platform for machine learning ” (Google self-promotion), TensorFlow allows both beginners and professionals customized API connections to deal with the development of AI systems.

The name “Tensor” comes from the computational operations that take place on artificial neural networks. TensorFlow is already used internally by Google, which is why it is a very sophisticated platform and supports numerous programming languages.

Microsoft Cognitive Toolkit

Highly efficient and tailored to scalability – this is the best way to describe the Microsoft Cognitive Toolkit, or CNTK for short. To ensure this, the framework is of course optimized for use on Microsoft’s Azure.

Its strengths lie above all in the real-time analysis of data. Microsoft itself has long used the toolkit in services such as Cortana or Skype, which means that the Cognitive Toolkit can be used wherever large amounts of data need to be analyzed.

PyTorch

PyTorch, too, originally came from one of the big players: Developed by Facebook engineers from the Torch environment that has existed since 2002, the open source framework has now become one of the standard tools for AI development.

OpenAI also uses the open source framework in the meantime, as it offers many possibilities for the development of intelligent systems. For example, there are libraries for all important areas of machine learning, including image recognition, speech processing, pattern recognition, and the training of neural networks. A solid basis to realize machine learning projects.

Shogun

Shogun is an open source library for AI development under GPLv3 license. The toolbox written under C++ provides developers with a variety of tools to design machine learning applications.

In addition to Interfaces for popular programming languages such as Python, Java, Ruby and C#, there is support for various Vector-models, as well as Cluster – and Online-Learning Algorithms. During development, bioinformatics was also kept in mind, which enables Shogun to process enormous amounts of data.

FluxML

FluxML is a Machine-Learning Framework under the mit license. It describes itself as the” elegant machine learning stack ” and is therefore gladly taken on hand by AI researchers, which is why well-known universities are also involved in the project.

Flux is designed to make Machine Learning applications intuitively and mathematically, which is why the Framework also relies on Julia as a programming language. FluxML has a variety of packages and scripts that provide specific features, including GPU and TPU support.

Apache Mahout

The Apache Foundation is best known for its web server. However, there is also an AI framework with Mahout, which is particularly suitable for the development of statistical and mathematical machine learning applications. This is made possible by the use of linear algebra, whereby a significant effect can be achieved with a few lines of code.

Mahout also uses Java as a basis for the scalability of the developed applications. Unlike other ML toolkits, however, Mahout comes with its own R-like language without translating other languages, which can make conversion of existing solutions difficult.

Deeplearning4j

Deeplearning4j is also based on Java and can be used for the development of deep learning applications and neural networks. Thanks to its efficient handling of distributed computing via CPU and GPU via Spark and Hadoop, it is possible to develop particularly powerful and easily scalable algorithms here.

scikit-learn

The machine learning library scikit-learn is based on Python and is particularly suitable for predictive data analysis: various algorithms for classifying and sorting data are on board, the entire library is optimized to work together with Python and the associated scientific libraries NumPy and SciPy. Because of this, and because it is popular in teaching, scikit-learn is one of the most important and interesting tools for AI development.

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