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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 ...
ML model checkpoints versioning: The new release also enables versioning of all checkpoints with corresponding code and data. Metrics logging: DVC 2.0 introduced a new open-source library DVC-Live that would provide functionality for tracking model metrics and organizing metrics in a way that DVC could visualize with navigation in Git history.
Supported data models (conceptual, logical, physical) Supported notations Forward engineering Reverse engineering Model/database comparison and synchronization Teamwork/repository Database Workbench: Conceptual, logical, physical IE (Crow’s foot) Yes Yes Update database and/or update model No Enterprise Architect
Comparison of agent-based modeling software; Comparison of chess video games; Comparison of EDA software; Comparison of force-field implementations; Comparison of GUI testing tools; Comparison of Java virtual machines; Comparison of MQTT implementations; Comparison of optimization software; Comparison of OS emulation or virtualization apps on ...
Data about applicant's family and various other factors included. 12,960 Text Classification 1997 [480] [481] V. Rajkovic et al. University Dataset Data describing attributed of a large number of universities. None. 285 Text Clustering, classification 1988 [482] S. Sounders et al. Blood Transfusion Service Center Dataset
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To make the data amenable for machine learning, an expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods. After these steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their model.
It is mostly used for numerical analysis, computational science, and machine learning. [6] C# can be used to develop high level machine learning models using Microsoft’s .NET suite. ML.NET was developed to aid integration with existing .NET projects, simplifying the process for existing software using the .NET platform.