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Software Creator Initial release Software license [a] Open source Platform Written in Interface OpenMP support OpenCL support CUDA support ROCm support [1] Automatic differentiation [2] Has pretrained models Recurrent nets Convolutional nets RBM/DBNs Parallel execution (multi node) Actively developed BigDL: Jason Dai (Intel) 2016 Apache 2.0 ...
Neural network simulators are software applications that are used to simulate the behavior of artificial or biological neural networks. They focus on one or a limited number of specific types of neural networks. They are typically stand-alone and not intended to produce general neural networks that can be integrated in other software.
With the release of version 0.3.0 in April 2016 [4] the use in production and research environments became more widespread. The package was reviewed several months later on the R blog The Beginner Programmer as "R provides a simple and very user friendly package named rnn for working with recurrent neural networks.", [5] which further increased usage.
OpenNN (Open Neural Networks Library) is a software library written in the C++ programming language which implements neural networks, a main area of deep learning research. [1] The library is open-source , licensed under the GNU Lesser General Public License .
Neural Engineering Object (Nengo) is a graphical and scripting software for simulating large-scale neural systems. [1] As the neural network software Nengo is a tool for modelling neural networks with applications in cognitive science , psychology , artificial intelligence and neuroscience .
In various studies, authors have claimed that neural networks used for generating trading signals given various technical and fundamental inputs have significantly outperformed buy-hold strategies as well as traditional linear technical analysis methods when combined with rule-based expert systems. [29] [30] [31]
NNI (Neural Network Intelligence) is a free and open-source AutoML toolkit developed by Microsoft. [3] [4] It is used to automate feature engineering, model compression, neural architecture search, and hyper-parameter tuning. [5] [6] The source code is licensed under MIT License and available on GitHub. [7]
Networks such as the previous one are commonly called feedforward, because their graph is a directed acyclic graph. Networks with cycles are commonly called recurrent. Such networks are commonly depicted in the manner shown at the top of the figure, where is shown as dependent upon itself. However, an implied temporal dependence is not shown.