Digital Cities Roadmap. Группа авторов
Читать онлайн книгу.
learning method is facilitated across a number of ML frameworks and resources. The challenge of choosing the best platform in order to data analytics flow sharing can also be achieved from alternative viewpoints challenging despite the growing amount of such toolkits. There is generally no one toolkit that completely suits all challenges (Table 1.6) and includes remedies. Some of the toolkits available could overlap, with benefits and drawbacks.
Table 1.6 Difference between deep learning and machine learning tools [56].
| Tool | Creator | OS | Open source? | Written In | Interface | CUDA support? | Algorithms | Release date |
| Tensor Flow | Google Brain team | Linux. Mac OS X (Windows support on road map | Yes | C++, Python | Python, C/ C++ | Yes | Deep learning algorithm: RNN, CN, RBM and DUN. | Novembeir 2015 |
| Theano | Universit de Montral | Cross-platform | Yes | Python | Python | Yes | Deep learning algorithm: RNN, CN, RBM and DBN. | September 2007 |
| H20 | H20.ai | Linux, Mac OS, Microsoft Windows And Cross-platform inch Apache HDFS; Amazon EC2, Google Compute Engine, and Microsoft Azure. | Yes | Java, Scala, Python, R | Python, R | No | Algorithms for classification, clustering, generalized linear models, statistical analysis, ensembles, optimization tools, data pre-processing options and deep neural networks. | August 2011 |
| Deeplearning4j | Various. Original author Adam Gibson | Linux, OSX, Windows, Android, CyanogenMod (Cross-platform) | Yes | Java, Scala, C, CUDA | Java, Scala, CIo-jure | Yes | Deep learning algorithms including: RBM, DBN. RNN. deep autoencoder | August 2013 |
| MLlib Spark | Apache Software Foundation. UC Berkeley AMPLab, Databricks | Microsoft Windows, OS X, Linux | Yes | Scala. Java, Python, R | Scala, Java, Python. R | No | Classification, regression, clustering, dimensionality reduction, and collaborative filtering | May 2014 |
| Azure | Dave Cutler from Microsoft | Microsoft Windows, Linux | No | C++ | C++, Java. ASP.NET, PHP. Nodejs, Python | Yes | Classification, regression, clustering | October 2010 |
| Torch | Ronan Collobert, Koray Kavukcuoglu, Clement Farabet | Linux. Android, Mac OS XTiOS | Yes | C, Lua | Lua, LuaJlT. C, utility library for C++/ OpenCL | Yes | Deep algorithms | October 2002 |
| MOA | University of Waikato | Cross-platform | Yes | Java | GUI, the commandline. and Java | No | ML algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) | November 2014 |
| Caffe | Berkeley Vision and Learning Center. community contributors | Ubuntu, OS X, AWS, unofficial Android port, Windows support by Microsoft Research, unofficial Windows port | Yes | C++, Python | C++, command line. Python, MATLAB | Yes | Deep learning algorithms: CN, and RNN | December 2012 |
1.10.7 Big Data Research Applications for SBs in Real-Time
Many systems need a stream data processing in real time, so it is not feasible for this form of program to wait for data to be stored so evaluated. Stream processing is typically configured to interpret large volumes of data and operate on data streaming in real time by using constant queries, including SQL queries, to manage streaming data in real time, using an interface that is robust, accessible and fault resistant (Table 1.7).
1.10.8 Implementation of the ML Concept in the SB Context
Figure 1.25 illustrates specific measures to forecast an event in the SB sense by utilizing ML methods.
On the other hand, the aim of optimization is to optimize long-lasting gains by proper decisions. Strengthening learning with these issues can be used. Many optimization issues may be treated as predicting issues such that benefit is estimated for different activities and the activity with the largest income is chosen. The most important form in optimization is decision-making. A variety of factors and compromises about the effects of specific environmental locations need to be addressed.
Smart Building Services Taxonomy
The taxonomy of SB resources essential domains is shown in Figure 1.26. Lighting service connects the well-being of occupants in SBs that have sensors that save energy when lights are not needed, based on their operation. Power and electricity can supply a percentage of SB power consumption with renewable energy sources. HVAC implies the heating, ventilation and air conditioning device, built for the comfort of citizens and an efficient ambient contact. The water resources program aims at growing conservation and maximizing resource recovery for water supply.
Smart building service taxonomy is related to the maintenance of electronic doors, biometrics and SB security cameras devices. The Control Center offers management and decision-making for apps. The automated apps