Application types

• Approximation (or modeling) to discover intricate relationships.
• Classification (or pattern recognition) to recognize complex patterns.
• Forecasting (or time series prediction) to predict trends.

Data set

• Compatible with the most common data files: CSV, DAT, TXT, Excel, OpenOffice …
• Complete configuration of variables.
• Complete configuration of instances.
• Exhaustive descriptive statistics.
• Estimation of variables importance using many types of correlations.
• Innovative utilities for outlier detection and data filtering.

Neural network

• Network architecture with an unlimited number of layers.
• Perceptron layer with several activation functions: linear, hyperbolic tangent, logistic, and rectified linear.
• Probabilistic layer with different activation functions: binary, continuous, competitive, and softmax.
• Scaling layer with minimum/maximum and mean/standard deviation methods.
• Unscaling layer with minimum/maximum and mean/standard deviation methods.

Training strategy

• Loss indices for all types of applications:
  • Weighted squared error for common data sets.
  • Minkowski error for dealing with outliers.
  • Cross-entropy error for pattern recognition.
  • Weighted squared error for unbalanced data sets.
  • L1 and L2 Regularization for avoiding overfitting.
• Optimization algorithms for all volumes of data:
  • Stochastic gradient descent and adaptive moment estimation for huge data sets.
  • Gradient descent, conjugate gradient and adaptative linear momentum for the training of big data sets.
  • Quasi-Newton method for fast training of medium data sets.
  • Levenberg-Marquardt algorithm for high-speed training of small data sets.

Model selection

• Neurons selection algorithm for finding the optimal network architecture: incremental order.
• Inputs selection algorithms for selecting the most important features: growing inputs, pruning inputs, and genetic algorithm.

Testing  analysis

• Testing errors and corresponding statistics and histogram calculation.
• Linear regression analysis for functional regression problems.
• Confusion matrix for pattern recognition applications.
• Full set of metrics for the evaluation of binary classification tests.
• ROC curve for diagnostic tests.
• List of misclassified instances.
• Cumulative gain and lift charts for segmentation applications in marketing.
• Profit chart for simulating marketing campaigns.

Model deployment

• Calculation of output values.
• Directional plots for exploring the predictive model.
• Exportable mathematical expression of the model.
• Exportable predictive model in C and Python.

Output

• Exhaustive results in interactive report plenty of descriptions, tables, and figures.
• Report exportable to Word and Pdf formats.
• Results exportable in data format.

Help

• Simple user’s guide.
• Extensive tutorials on the application of neural networks with Neural Designer.
• Several step-by-step solved examples of machine learning applications in different fields.
• Premium technical support by email, phone, or video chat.

Performance

• Software developed with the high-performance programing language C++.
• Code subjected to optimization techniques for memory management and processing speed.
• CPU parallelization employing OpenMP.
• GPU acceleration with CUDA, MKL and Eigen.

Supported platforms

• Windows.
• Mac OS X.
• Linux (Debian and Ubuntu).

Cloud computing

• Amazon Web Services (AWS) marketplace.