Software Neuro Laboratory, and the LIONsolver integrated software. Free open source component based environments include Encog and Neuroph. Criticism A disadvantage of component-based development environments is that they are more complex than simulators. They require more learning to fully operate and are more complicated to develop. Custom neural networks The majority implementations of neural networks available are however custom implementations in various programming languages and on various platforms. Basic types of neural networks are simple to implement directly. There are also many programming libraries that contain neural network functionality and that can be used in custom implementations (such as TensorFlow, Theano, etc., typically providing bindings to languages such as Python, C++, Java). Standards In order for neural network models to be shared by different applications, a common language is necessary. The Predictive Model Markup Language (PMML) has been proposed to address this need. PMML is an XML-based language which provides a way for applications to define and share neural network models (and other data mining models) between PMML compliant applications. PMML provides applications a vendor-independent method of defining models so that proprietary issues and incompatibilities are no longer a barrier to the exchange of models between applications. It allows users to develop models within one vendor's application, and use other vendors' applications to visualize, analyze, evaluate or otherwise use the models. Previously, this was very difficult, but with PMML, the exchange of models between compliant applications is now straightforward. PMML consumers and producers A range of products are being offered to produce and consume PMML. This ever-growing list includes the following neural network products: R: produces PMML for neural nets and other machine learning models via the package pmml. SAS Enterprise Miner: produces PMML for several mining models, including neural networks, linear and logistic regression, decision trees, and other data mining models. SPSS: produces PMML for neural networks as well as many other mining models. STATISTICA: produces PMML for neural networks, data mining models and traditional statistical models. See also AI accelerator Physical neural network Comparison of deep learning software Data Mining Integrated development environment Logistic regression Memristor References External links Comparison of Neural Network Simulators at University of Colorado Applications of artificial intelligence Artificial neural networks