Online chats, predictive analytics, and disease diagnostics: we face neural networks quite frequently. So, what is their concept? Why should companies analyze data produced by artificial intelligence or by users?

The article explains what artificial neural networks and big data mean, why they are required, and how to work with them.

Machine Learning: How is It Related
to Artificial Neural Networks?

Artificial neural network is a human-created computational model with a huge amount of consequently operating processes united by numerous nodes similar to the human neuron. These artificial neurons are distributed in the network by layers of three types: an external layer, an internal layer, and an output layer.

Information initially goes to input (external) layer neurons that accept and transfer it to hidden (internal) layer neurons. The hidden layer accounts for the information primary processing. Afterwards, data are sent to the final output layer. Neural networks with more than one hidden layer are called deep.

Besides, there are two types of networks:

1. feedforward neural networks: all connections strictly move from input layers to their outputs;
2. recurrent neural networks: data from hidden layer output neurons are partially transferred to the first layer or other layer with a smaller number. Such networks are often applied to process sequences: genome data, text processing, and speech recognition.

At the same time, neural networks can be used for various tasks: equipment deterioration analysis, document processing, communication with customers, marketing activities, etc.

Neural network algorithms are trained like kids. Mature artificial intelligence is created using machine learning (ML): a class of methods for solving certain issues. There are several types of ML:

1. supervised training where an experimental system is forcefully taught by stimulus-response examples;
2. unsupervised learning where an experimental system is spontaneously taught by performing the given task without tester’s intervention;
3. reinforcement learning where an experimental system is taught by interacting with certain environment.

To work with neural networks, one can apply the following tools:

• Scikit-learn: a library for the Python programming language that assists in making mathematical calculations. It can be combined with other libraries, such as NumPy, SciPy, and Matplotlib, in order to design an interactive app within a development environment or integrated into other software;
• Hadoop: an open-source framework allowing to divide an app into several fragments. Each fragment is processed on any node (computer) in the computing system cluster. Hadoop is a standard software for Big Data analysis;
• RapidMiner: an open-source environment allowing to make forecasts and conduct analytics. The platform supports all stages of deep data analysis and Hadoop features.

Big Data Concept and Calculation

Big Data analysis is a complex of actions aimed at collection, analysis, and systematization of data, which volume exceeds 100 GB. The Big Data analysis includes network technologies, servers, software, and technical services.

Big Data is used to:

• store and manage the data amount of hundreds of terabytes or petabytes that cannot be processed by relational databases;
• organize unstructured data consisting of text information, images, photos, videos, etc.;
• generate analytical reports and integrate predictive models.

Big Data analysis is a combination of programming and analytics, thus one applies a range of software to work with it. Given below are some of them:

• SAS Eminer: a descriptive and predictive modeling system;
• Tableau: a data visualization program;
• SPSS: a predictive analytics software;
• Zoho Reports: an online reporting program;
• NodeXL: an interactive tool for network visualization and analysis;
• Excel: unexpectedly, but the old school product by MS Office is pretty good in Big Data analytics;
• SQL: a programming language for creating, modifying, and managing data in the relational database;
• Python: a programming language designed for analytics.

The data processing results look like a statement of recommended practice. Besides, its nature cannot be predicted until the end of processing. Sometimes even the smallest changes can affect the ultimate picture. However, this technology will multiply company profits in case of proper application.

You will be able to discover more about benefits of neural networks and Big Data analytics for businesses
at AI Conference Kyiv that will take place on June 4 in the capital of Ukraine.

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