Bahrudin Hrnjica

Bahrudin Hrnjica

ANNdotNET is an open source project for deep learning written in C# with ability to create, train, evaluate and export deep learning models. The project consists of the Graphical User Interface module capable to visually prepare data, fine tune hyper-parameters, design network architecture, evaluate and test trained models. The ANNdotNET introduces the Visual Network Designer, (VND) for visually design almost any sequential deep learning network. Beside VND, ANNdotNET implements Machine Learning Engine, (MLE) based on CNTK - deep learning framework, with ability to train and evaluate models on GPU. For model evaluation ANNdotNET contains rich set of visual and descriptive performance parameters, history of the training process and set of export/deployment options. The advantage of using ANNdotNET over the classic code based ML approach is more focus on deep learning network design and training process instead of focusing on coding and debugging. It is ideal for engineers not familiar with supported programming languages. The project is hosted at github.com/bhrnjica/anndotnet.

Bahrudin Hrnjica

Daany is .NET and cross platform data analytics and linear algebra library written in C# supposed to be a tool for data preparation, feature engineering and other kind of data transformations and feature engineering. The library is implemented on top of .NET Standard 2.1 and supports .NET Core 3.0 and above separated on several Visual Studio projects that can be installed as a NuGet package. The library implements DataFrame as the core component with extensions of a set of data science and linear algebra features. The library contains several implementation of time series decomposition (SSA, STL ARIMA), optimization methods (SGD) as well as plotting support. The library also implements set of features based on matrix, vectors and similar linear algebra operations. The main part of the library is the Daany.DataFrame with similar implementation that can be found in python based Pandas library. The paper presents the main functionalities and the implementation behind the Daany packages in the form of developer guide and can be used as manual in using the Daany in every-day work. To end this the paper shows the list of papers used the library.