Advanced analytics at your hands

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1 2.3 Advanced analytics at your hands Neural Designer is the most powerful predictive analytics software. It uses innovative neural networks techniques to provide data scientists with results in a way previously unachievable. Predictive analytics is a technique that uses data available for organizations in order to predict with maximum certainty what will happen in the future. As a result, companies can anticipate and take appropriate decisions. TARGET APPLICATIONS Business intelligence Recently, new disruptive techniques for machine learning, such as deep learning, have been developed. Deep learning combines the best methods from artificial intelligence and data science to build powerful predictive models from large-scale data sets. Sales forecasting Customer segmentation Churn prevention Risk analysis Etc. Health care Organizations generate large amounts of data. A rising volume, velocity and variety are the three key features of big data. Therefore, companies need tools that are able to analyze more data in less time. They also need more reliable predictions to increase their competitive advantage. Neural Designer is an innovative solution for predictive analytics based on neural networks. It makes intelligent use of data by discovering complex relationships, recognizing unknown patterns or predicting actual trends. Medical diagnosis Medical prognosis Microarray analysis Protein design Etc. Industry 4.0 Output prediction Performance optimization Predictive maintenance Quality improvement Etc.

2 KEY BENEFITS Easy to use With Neural Designer there is no need for programming or building complicated block diagrams. Its user interface has been conceived to simplify data entry as well as the execution of the different tasks. The software comes with many tutorials and examples to help you learn how to use it, and our team provides specialized technical support to our customers through telephone, and Skype. Great visualization of results The Neural Viewer writes a report displaying comprehensive results from all tasks. Users can explore data and visualize the results with tables, charts and pictures that can be exported in order to be used in other tools. Also, the whole report displayed in the viewer can be exported to PDF format.

3 High performance Neural Designer outstands in terms of performance. The next figures summarize a capacity, accuracy and speed comparison among a data mining platform written in Java, a predictive analytics tool written in R and Neural Designer, which is written in C++. As we can see, Neural Designer is able to analyze data sets 100 to 1000 times bigger, is about 8 to 12 times faster and is 1000 times more accurate than its Java and R competitors. (The data-set to make the comparisons is available at ) Simple deployment Once the model is obtained, Neural Designer provides its mathematical expression. An example of that expression in the R programming language is written below. expression <- function(x) { scaled_x<-2*(x+1)/(1+1)-1 y_1_1<-tanh( *scaled_x) y_1_2<-tanh( *scaled_x) y_1_3<-tanh( *scaled_x) scaled_variable_2<-( *y_1_ *y_1_ *y_1_3) outputs <- c(0.5*(scaled_variable_2+1.0)*(1+1)-1) outputs }

4 The same expression in Python is listed next. from math import tanh def expression (x) : scaled_x=2*(x+1)/(1+1)-1 y_1_1=tanh( *scaled_x) y_1_2=tanh( *scaled_x) y_1_3=tanh( *scaled_x) scaled_variable_2=( *y_1_ *y_1_ *y_1_3) (variable_2) = (0.5*(scaled_variable_2+1.0)*(1+1)-1) return variable_2 The mathematical expression gives you direct access to your predictive model. It can be easily embedded into other software or any computer language. TECHNICAL FEATURES Application types Data set Neural network Loss index Function regression (or modelling) to discover intricate relationships. Pattern recognition (classification) to recognize complex patterns. Time series prediction (forecasting) to predict current trends. Compatible with the most common data files: Excel, OpenOffice, CSV, Weka, DAT, TXT Also compatible with the most common data bases: Oracle, MySQL, SQLite, SQL Server and Access. Complete configuration of variables and instances. Exhaustive descriptive statistics. Estimation of variables importance by means of linear/logistic correlations. Advanced methods for data balancing. Innovative utilities for outlier detection. Network architecture with unlimited number of layers. Threshold, symmetric threshold, logistic, hyperbolic tangent and linear activation functions. Scaling and unscaling layers with minimum/maximum and mean/standard deviation methods. Probabilistic layer with binary and softmax methods. Sum squared error, mean squared error, root mean squared error and normalized squared error functionals for common data sets. Minkowski error for dealing with outliers. Cross-entropy error for pattern recognition. Weighted squared error for unbalanced data sets. Regularization for avoiding overfitting.

5 Training strategy Model selection Gradient descent and conjugate gradient for training of big data sets. Quasi-Newton method for fast training of medium data sets. Levenberg-Marquardt algorithm for very fast training of small data sets. Incremental order and simulated annealing for finding the optimal network architecture. Growing inputs, pruning inputs and genetic algorithm for selecting the most important features. Testing analysis Model deployment Output Help Performance Complete error data and corresponding statistics calculation. Linear regression analysis for function regression problems. Confusion matrix for pattern recognition applications. Full set of metrics for evaluation of binary classifiers. ROC curve for diagnostic tests. Cumulative gain and lift charts for segmentation applications in marketing. Calibration plot for classification problems. Error autocorrelation and cross-correlation for time series prediction. List of misclassified instances. Calculation of output values. Directional plots for exploring the predictive model. Jacobian values. Exportable mathematical expression of the model. Exportable predictive model in R and Python. Exhaustive results in an interactive report plenty of descriptions, tables and figures. Report exportable to Word and Pdf formats. Results exportable in data format. 6 extensive tutorials on the application of deep learning with Neural Designer. 14 examples of machine learning applications in different fields. Premium technical support by , phone or video chat. Software developed with the high performance programing language C++. Code subjected to optimization techniques for memory management and processing speed. CPU parallelization by means of OpenMP. GPU acceleration with CUDA.

6 Supported platforms Windows. Mac OS X. Linux (Debian and Ubuntu). AVAILABILITY Neural Designer is available directly from the vendor. Please visit our web site at or contact our sales department at Neural Designer and the Neural Designer logo are trademarks of Artificial Intelligence Techniques, Ltd Artificial Intelligence Techniques, Ltd.

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