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                                    NeuroIntelligence

                                    NeuroIntelligence

                                    $ 497
                                    Personal Single User
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                                    $ 4970
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                                    Neural networks software

                                    NeuroIntelligence is a neural networks software application designed to assist neural network, data mining, pattern recognition, and predictive modeling experts in solving real-world problems. NeuroIntelligence features only proven neural network modeling algorithms and neural net techniques; software is fast and easy-to-use.

                                    All neural network design stages supported

                                    NeuroIntelligence supports all stages of neural net design and application. Software can be used to:
                                    1. analyze and pre-process datasets;
                                    2. find the best neural network architecture;
                                    3. train, test and optimize neural networks;
                                    4. apply the designed neural net to new data.

                                    Neural network software with advanced visualization

                                    Visualized architecture search, neural network training and testing. Neural network architecture search: fitness bars, network training graphs comparison. Training graphs: dataset error, network error, weights and errors distribution, neural network input importance. Testing: actual vs. output graph, scatter plot, response graph, ROC curve, confusion matrix.

                                    Improve your productivity

                                    The interface of NeuroIntelligence is optimized to solve data mining, forecasting, classification and pattern recognition problems. You can create a better solution much faster using the tool's easy-to-use GUI and unique time-saving capabilities.
                                    Many processes are automated but you can easily understand the underlying behavior with graphs, statistics and reports. Options and parameters are intelligible, comprehensive and accessible with one mouse click.  

                                    Data analysis and preprocessing

                                    Save your time with automated column identification and encoding. Identify and encode date/time columns. Perform automatic and manual data partition. Identify and replace/remove data anomalies. Analyze your data with response graph.
                                     

                                    Automated network selection

                                    Select fitness criteria along with architecture search method and NeuroIntelligence does the rest. It automatically finds the best architecture offering you graphs of the search process and details for every tested neural network.
                                     

                                    Proven training algorithms

                                    The most efficient algorithms, such as Conjugate Gradient Descent, Levenberg-Marquardt, Quick-Propagation, variations of Quasi-Newton and Back-Propagation, are available for neural network training.
                                     

                                    Proven techniques

                                    Improve network performance with early-stopping, generalization loss control, outliers and missing values handling, dataset partition, jogging weights, different activation and error functions, weights initialization and more.
                                     

                                    Ease of use

                                    NeuroIntelligence was developed with the highest attention to usability. It has intelligible layout, options, reports and descriptions. It offers graphs and results visualization at all stages. You can save all graphs and tables, as well as other results with a single mouse click. Local toolbars and online help are always accessible.
                                     

                                    Application in several clicks

                                    After a neural network is tested it can be easily applied to new data. Results are visualized with a response graph. You can apply the selected network to a single case, data file or records from your input dataset. The whole project or only selected neural network can be saved for future use.

                                    Key features

                                    • Create and apply neural networks to:
                                      • Forecasting
                                      • Classification
                                      • Function Approximation
                                      • Data Anomalies Detection
                                    • Analyze and preprocess datasets
                                    • Automatically search for the best neural network architecture
                                    • Analyze network performance with graphs and detailed statistics
                                    • Easy-to-use interface

                                    Analyze and Pre-process Your Data

                                    • Import Excel files
                                    • Import popular ASCII file formats (CSV, TXT, PRN)
                                    • Custom date formats and file structure definition
                                    • Input dataset size is limited only by the hardware of the computer

                                    • Date/Time values encoding
                                    • Categorical values encoding
                                    • Numeric values scaling
                                    • Min/max values specification for numeric columns scaling

                                    • Missing values handling for both numeric and categorical data
                                    • Outliers handling for numeric data
                                    • Automatic recognition of data entry errors (wrong type values)
                                    • Visual representation of data anomalies in the Dataset window

                                    • Automatic and manual column type identification (numeric, categorical, date, time, text)
                                    • Random, sequential and manual dataset partition onto training, validation and test sets
                                    • Accept/ignore records and columns manually

                                    • Statistical information for data columns
                                    • Binary columns for anomalies indication
                                    • Two methods of automatic lag columns insertion
                                    • Preprocessed data representation
                                    • Detailed Data Analysis and Data Preprocessing Reports

                                    Design Neural Network

                                    • Input feature selection (GA, stepwise, exhaustive).
                                    • Manual architecture specification (up to 5 hidden layers for multi-layer perceptron)
                                    • Heuristic architecture search with customizable range of search and sensitivity
                                    • Exhaustive architecture search
                                    • Customizable search range and search sensitivity
                                    • Detailed statistics for each tested architecture
                                    • Network fitness criteria: AIC, Test set error, Correlation, R-squared
                                    • Graphical representation of network fitness
                                    • Time-series networks
                                    • Network sets
                                    • Network visualization

                                    • Training algorithms: Conjugate Gradient Descent, Levenberg-Marquardt, Quick-Propagation, Quasi-Newton, Quasi-Newton (Limited Memory), Incremental and Batch Back-Propagation
                                    • Automatic adjustment of learning rate and momentum for Back-Propagation algorithm
                                    • Activation functions: Linear, Logistic, Tanh, Softmax
                                    • Error functions: Sum-of-Squares, Cross-entropy
                                    • Classification model: Winner-takes-all, Confidence-limits (Accept/Reject levels)

                                    Control Network Training Process

                                    • Real-time training error graph
                                    • Real-time control on training parameters:
                                      • errors on training and validation set: MSE, MAE, CCR
                                      • error improvement
                                      • training speed (iterations per second)
                                      • # of iterations.
                                    • Continue training with new parameters
                                    • Jog weights
                                    • Add jitter

                                    • Correlation and r-squared real-time graphs
                                    • Error improvement graph
                                    • Weights distribution graph
                                    • Error distribution graph
                                    • Input importance graph
                                    • Training log: test and validation set error for each iteration

                                    • Early-stopping on generalization loss
                                    • Retain and restore best network
                                    • Stopping conditions:
                                      • target error on training and validation sets: MSE, MAE, CCR
                                      • error improvement: network error, dataset error
                                      • number of iterations
                                      • generalization loss
                                    • Automatic network retrains and selection of the best network among retrains
                                    • Retrains statistics
                                    • Weights initialization: manual randomization range; optimized for Uniform or Gaussian distribution

                                    Test and Analyze Performance

                                    • Actual vs Output graph
                                    • Scatter plot
                                    • Response graph
                                    • Confusion matrix
                                    • ROC curve
                                    • Actual vs Output Table with absolute and relative errors
                                    • Input importance graph

                                    Apply Network

                                    • Enter new cases manually or insert from the Clipboard
                                    • Load new cases from a new data file
                                    • Apply to selected records from your original dataset
                                    • Graphical network output representation
                                    • Output representation with Results Table
                                    • Confidence limits for network output
                                    • Save results in a separate file or copy them to the Clipboard

                                    General

                                    • Customizable interface
                                    • Detailed reporting
                                    • Online help system
                                    • Free technical support
                                    • Project files to keep all related information in one place
                                    • Sample financial, marketing, real estate and scientific problems included

                                    Following are the minimum installation requirements:

                                    • Pentium-compatible processor (Pentium II or higher recommended)
                                    • Windows® 98, ME, 2000, XP, Vista or Windows 7.
                                    • Internet Explorer 5.0 or higher
                                    • 64 MB available RAM (128 MB recommended)
                                    • 256-color monitor capable of 800 x 600 screen resolution
                                    • 15 MB of free hard disk space

                                    All Alyuda Software is Free for:


                                    Charity organizations

                                    Microfinance and student lending

                                    Sustainable development projects
                                    Licensing

                                    For more information about product licensing, please visit Alyuda licensing.

                                    The 30-day Trial Policy

                                    Alyuda NeuroIntelligence, Alyuda Forecaster XL and Alyuda Forecaster can be downloaded and used as free trial versions during a 30-day period. Alyuda NeuroFusion provides with detailed Help file which enables the user to easily understand the library. Once you have purchased one of those products, no refunds will be issued for your order(s).

                                    Delivery

                                    Once your purchase has been approved, we will email you a personalized license key along with download instructions within the next 24 hours (Mon-Fri). When purchasing please supply your valid e-mail address.

                                    Educational Discounts

                                    If you are a Teacher, Student or another Educational Professional, you can claim 30% off on Alyuda products and services.

                                    To take advantage of this discount is simple.

                                    You need to send proof of status, preferable scanned and emailed to salesdept@www.sxxmjz.cn. This can be in a form of an school ID card. Any school ID, union ID or educational union ID card is acceptable.

                                    We reserve the right to charge full price for products and services purchased at a discount if requested proof of status/age is not provided in a timely manner.

                                    Volume Discounts

                                    If you need 2 or more licences please contact Sales Department to get volume discount.

                                    More Ordering Options

                                    You can order by mail, phone, fax as well as pay by check or bank wire transfer. Contact our Sales Department for detailed instructions. If you transfer funds by wire please include an addition $20 bank fee.

                                    Using a Purchase Order

                                    Alyuda accepts purchase orders from the following entities (in the USA and Canada only):

                                    • Government Agencies
                                    • Educational Institutions
                                    • Companies with a D&B rating
                                    Email or fax us a copy of the purchase order. We normally ship within one business day.

                                    Contact Us

                                    We will be glad to answer all of your questions. Please fill the form below, contact our Sales Representative or contact directly the appropriate department.

                                    NeuroIntelligence

                                    NeuroIntelligence

                                    Please note: you are eligible for free technical support during 30-day evaluation of Alyuda NeuroIntelligence.

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