Artificial Neural Networks modelling has become an important area of advanced analytics and machine learning. The area is both exciting and intriguing, helping to generate valuable analytical insights to support decision making and advanced research. Given the importance of this foundation in the advanced analytics, machine learning and data science, AroniSmartIntelligence ™, one of the most advanced analytics tools with Big Data, Statistics, Econometrics, Machine Leaning, Text and Sentiment analysis, Support Vector Machine capabilities has implemented a module on Neural Network, starting with Multi-Layer Perceptron Models.
Tools have been developed to leverage existing knowledge, implement algorithms and facilitate the rather specialized concepts and data structures Neural Network Models are proven concepts and methodologies in Artificial Intelligence and Deep Learning. They are additional stong analytical methods to help advanced analytics experts, statisticians, econometricians, machine learning analysts, data scientist, and big data analysts to find the best models to address their business and research needs..
Neural Networks and Deep Learning
Neural Network Models are very important in Deep Learning. There are three main types of Neural Network structures::
- Artificial Neural Network (ANN) also known as Feed Forward Neural Network;
- Recurrent Neural Network (RNN). This includes a looping constraint on the hidden layer of nodes;
- Convolution Neural Network (CNN). CNN have kernels to extract relevant features from input layers.
The different Neural Network models have their own special applications, advantages and limitations. AroniSmartIntelligence Neural Network Module is focusing on ANN for multiple reasons:
- importance of being able to clearly explain the results;
- openness to any type of numerical structured input;
- searching for optimal solution;
- capability of leaning any non linear function, by introducing non linear data and properties trough inputs and the network;
- the ability to solve problems stochastically, to allow approximate solutions for extremely complex problems.Test
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AroniSmartIntelligence, the leading tool for Advanced Analytics, Machine Learning & Data Science
Statisticians, Data Scientists, Business and Financial Analysts, Savvy Investors, Engineers, Researchers, Students, Teachers, Economists, Political Analysts, and most of the practitioners use Advanced Analytics to answer questions, to support informed decision making or to learn.
AroniSmartIntelligence™ is a leading Advanced Analytics, Machine Learning and Data Science tool, with optimized cutting edge Statistics models, Econometrics, Big Data and Text Analytics.
AronismartIntelligence™ includes modules covering Machine Learning, Big Data mining, Bayesian Statistics, Neural Network Models, Unstructured Text Analysis, Sentiment and Emotion Analytics, and other advanced analytics.
What is Neural Network, Multi-Layer Perceptrons
There is often an ambivalence between the concept of Artificial Neural Networks and Multi-Layer Perceptrons. The ambivalence usually comes from the fact that multi-layer perceptrons may arguably be the most useful type of Neural Network models. In fact, a perceptron is a single neuron model, on which more advanced and larger neural networks are or can be built.
AroniSmartIntelligencet™ Neural Network Multi-Layer Perceptron.
With the Neural Network MultiLayer Perceptron, AroniSmartIntelligence tool can now help the advanced analytics experts, statisticians, econometricians, machine learning analysts, data scientist, and big data analysts to find the best models to address their business and research needs.
The multilayer perceptron (MLP) implemented in AroniSmartIntelligence uses at least three layers of nodes, with some potential modifications given the types of inputs and data structures. The input nodes depend on the data attributes (variables) and each node can use a linear or nonlinear activation function. MLP utilizes a supervised back-propagation learning technique for training. Depending on the output attribute, a linear and sigmoid output node would be used.
In setting up the model in AroniSmartIntelligence, the analyst/modeler/data scientist will choose the number of nodes in the hidden layer or let the network find the optimal artificial neurons. The user interface is intuitive and does not require any coding. The data required is just in the usual Aroni straightforward format.
AroniSmartIntelligence Neural Network MultiLayer Perceptron will help in creating mathematical statistics models to complement usual regression analysis capabilities and help in both categorical classification algorithm or linear sigmoid analyses
With the Neural Network MultiLayer Perceptron and Support Vector Machines modules, AroniSmartIntelligence user has complete solutions to address most aspects of deep learning, from much simpler support vector machines. Interest in backpropagation networks returned due to the successes of deep learning.
AroniSmartIntelligence™ Neural Network Analysis Examples
Example 1: AroniSmartIntelligence™ Neural Network Module
Example 2: AroniSmartIntelligence™ Neural Network: Multilayer Perceptron Model setup
Example 3: AroniSmartIntelligence™ Neural Network: Multilayer Perceptron Output
Example 4: AroniSmartIntelligence™ Neural Network: Multilayer Perceptron Output: Key Statistics
Example 5: AroniSmartIntelligence™ Neural Network: Multilayer Perceptron Output: Key Statistics with ANOVA
What is next?
The new version of AroniSmartIntelligence™ that includes the Artificial Neural Network module is available on Apple's App Store®.
More detailed information on the capabilities of AroniSmartIntelligence™ are available in the tool on Apple®'s App Store and on the AroniSoft web site(http://www.aronisoft.com).
More detailed use cases, real life applications, and AroniSmart Tools in actions can be found on on the AroniSmart web site (http://www.aronismart.com). Check the following tabs: ABOUT US, SOLUTIONS, SCITECH, MONEY.
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