10 Open Source and Free Tools for AI developers

Get started in AI and Machine Learning, part 2 10 Open Source and Free Tools for AI developers

The outstaffing development of AI and Machine Learning systems is time-consuming. Fortunately, there are a number of Free Tools, the software developers at work can help. We show what the are.

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The entry into the field of AI development with some help of Free and Open-Source Tools.The entry into the field of AI development with some help of Free and Open-Source Tools.

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Who wants to deal with the development of Machine Learning and AI systems, it takes a lot of Practice and some skills in the area of AI. The latter are, in many ways, a question of personal character traits.

Exercise can, however, build up, and some free Free can help platforms and Tools for AI development. It is also about experimenting and KI-‘t need to build Software from scratch.

OpenAI Gym

The Non-Profit organization OpenAI is a Microsoft and Elon Musk supported Initiative to help develop artificial intelligence is a useful helper for humanity. Some of the great thinkers such as Stephen Hawking, have, or had, in fact, a legitimate fear that a self-improving AI could be quickly to a threat to humanity.

A dystopian scenario, counter, supports OpenAI, the research and development of helpful artificial intelligence. AI developers can take advantage of this idea: in addition to the idea to provide as much as possible AI technology, open source, and you can use it with the OpenAI Gym is a simple Toolkit to develop simple learning Algorithms.

In this way, can make developers in a playful way with the functioning of Deep-Learning Algorithms familiar. If you want more, you can employ over the OpenAI API with the language processing of AI-systems, a look at the Beta channel of OpenAI’s worth in any case.

Google Tensor Flow

Google also provides a free AI-platform available to developers Under the name of tensor flow to the search engine giant offers an open-source Library in Python for AI development. As “End-to-End Open Source platform for machine Learning” (Google’s own advertising) allows you to tensor flow for beginners as well as professionals and custom API connections to deal with the development of AI systems.

The Name “Tensor” comes from the arithmetic operations that take place on artificial neural networks. Tensor flow is already Google-internal use, which is why it is a very Mature platform, and a wide range of languages supported.

Microsoft Cognitive Toolkit

Highly efficient and scalability trimmed – the Microsoft Cognitive Toolkit, short CNTK can be the best way to describe. To ensure the design is optimized for the Framework, of course, for use on Microsoft Azure.

Its Strengths lie especially in the real-time analysis of data. Microsoft itself uses the Toolkit already in services, such as Cortana or Skype, which makes the Cognitive Toolkit can be anywhere must be used where large amounts of data to be analyzed.

PyTorch

Also, PyTorch is originally from one of the big players From Facebook engineers from the already existing since 2002, Torch environment, it has morphed into an Open-Source Framework, in the meantime, one of the Standard Tools for AI development.

OpenAI uses the Open-Source Framework, in the meantime, also, since it offers many options for the development of intelligent systems. So, there are libraries for all major areas of Machine learning, including image recognition, speech processing, pattern recognition, or the Training of neural networks. A solid base so, in order to implement Machine-Learning projects.

Shogun

In the case of the Shogun is an Open-Source Library for AI development under the GPLv3 license. The C++ authored Toolbox provides developers with a variety of tools, to Machine-Learning design 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. In the development of bioinformatics has been kept in mind, which of the Shogun is able to process huge amounts of data.

FluxML

FluxML is a Machine-Learning Framework under the mit license. It referred to itself as the “elegant machine Learning Stack” and is therefore taken to the likes of AI researchers on the Hand, which is why well-known universities are 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, the specific features available, including GPU and TPU support.

Apache Mahout

The Apache Foundation is especially for your web server to be known. However, with the Mahout also an AI Framework that is particularly suitable for the development of statistical and mathematical Machine-Learning applications. This is made possible by the use of linear Algebra can be achieved, which, with a few lines of Code, a significant effects.

Mahout uses Java as the base in addition to the scalability of the developed applications. Unlike other ML Toolkits Mahout comes with its own R-like language without translating other languages, which can complicate the conversion of existing solutions.

Deeplearning4j

Also on Java, Deeplearning4j-based, and can be used for the development of Deep-Learning applications and neural networks. Due to its efficient use of distributed computing by the CPU and GPU via Spark and Hadoop, it is possible to develop a particularly powerful and scalable Algorithms.

scikit-learn

The Machine Learning library scikit-learn is based on Python and is especially useful for predictive data analysis: Different Algorithms for the classification and sorting of data are on Board, the whole library is optimized to work with Python and its scientific libraries NumPy and SciPy. As a result, and because it is popular in the teaching of application, is part of scikit-learn the most important and most interesting Tools for AI development.

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