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One of the greatest developments (and one of the reasons we started AIcompany) is the vast amount of open source materials available. You can use specifically designed libraries to build Machine and Deep Learning models and customise them to very specific needs. Get datasets from the web to easily train them or train them with your own company data.

We listed an overview of the many libraries and datasets for you.

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Theano logo.jpg
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Google's TensorFlow is currently the most popular open source software for AI algorithms. As a child of Google Deepmind, it is used by Google products and research. It is classified as 'back end' in the collection, because it is used as the architecture of a neural net in general and not as a specific machine or deep learning algorithm.

Theano is an open-source symbolic tensor manipulation framework developed by LISA/MILA Lab at Université de Montréal. It is more or less, the direct competitor of TensorFlow.

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley.

We didn't put Sklearn in the backend part, because many of it's plugins and API's that can be used on top of Theano or TensorFlow. Sklearn can, however, be used independently and work on its own.

Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras is mostly used for deep learning models.

Skymind's product Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. It is designed to be used in business environments, rather than as a research tool.

Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

Next to a interesting amount of open source software, a great amount of online datasets is available for free. For companies that are young and/or don't have sufficient amount of usable data, this is a relaxing thought. These sets can be used to train your self-build learning algorithm, so they'll be perfectly adjusted for your purpose.

Here are some resources: