Useful AI Tools and Frameworks

July 6, 2018

We have found a useful resource: DZone shares “10 Open-Source Tools/Frameworks for Artificial Intelligence.” We do like open-source software. The write-up discusses the advantages offered by each entry in detail, so navigate there to compare and contrast the options. For example, regarding the popular TensorFlow, writer Somanath Veettil describes:

“TensorFlow is an open-source software library, which was originally developed by researchers and engineers working on the Google Brain Team. TensorFlow is for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow provides multiple APIs. The lowest level API — TensorFlow Core — provides you with complete programming control. The higher level APIs are built on top of TensorFlow Core. These higher level APIs are typically easier to learn and use than TensorFlow Core. In addition, the higher level APIs make repetitive tasks easier and more consistent between different users. A high-level API like tf.estimator helps you manage data sets, estimators, training, and inference. The central unit of data in TensorFlow is the tensor. A tensor consists of a set of primitive values shaped into an array of any number of dimensions. A tensor’s rank is its number of dimensions.”

The rest of Veettil’s entries are these: Apache SystemML, Caffe, Apache Mahout, OpenNN, Torch, Neuroph, Deeplearning4j (the “j” is for Java), Mycroft, and OpenCog. I note that several options employ a neural network, but approach that technology in different ways. It is nice to have so many choices for implementing AI; now the challenge is to determine which system is best for one’s particular needs. This list could help with that.

Cynthia Murrell, July 6, 2018


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