A community is actively engaged to build and improve R and the plugins that are associated.

R also has a learning curve that is steep. R requires coding to a certain degree though R is a powerful software. The different aspects of data processing can be simplified by a great range of applications for which the toolboxes are available. The software package for statistical analysis used in the field of human behavior research and other fields and is available for free is R. The analysis can be automated by creating scripts and this option is included in the statistical package for the social sciences. The capability to compile descriptive statistics, parametric and non-parametric analysis, Graphical User Interface (GUI) to depict the results graphically is offered by statistical package for the social sciences. The most widely used software package for statistics within human behavior research is the Statistical Package for the social sciences.

Statistical Package for the social sciences (SPSS) Our theoretical results are verified by a series of numerical experiments including toy simulations and a real application of earthquake seismic intensity prediction.1. Based on these results, we prove that implementing the classical empirical risk minimization on deep nets can achieve the optimal generalization performance for numerous learning tasks. We present the adaptivity of features to depths and vice-verse via showing a depth-parameter trade-off in extracting both single feature and composite features. Our purpose is to quantify this feature-depth correspondence in feature extraction and generalization.

One of the most important challenge of deep learning is to figure out relations between a feature and the depth of deep neural networks (deep nets for short) to reflect the necessity of depth. Thus it has triggered enormous research activities in machine learning and pattern recognition.

Abstract : Deep learning is recognized to be capable of discovering deep features for representation learning and pattern recognition without requiring elegant feature engineering techniques by taking advantage of human ingenuity and prior knowledge.

Statistical tools for data analysis ppt