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Neural networks are a specific set of algorithms that inspired by the brain to store information. Artificial neural networks offer an alternative way to resolve complex and ill-defined problems. They are able to handle incomplete data and to deal with non-linear problems. After training they can perform predictions and generalisations at high speed.

Neural networks are used in many applications such as robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimisation, signal processing and social sciences. They are particularly useful in system modelling, such as in implementing complex mapping and system identification. Read More

Nowadays, there are many types of neural networks in deep learning which are used for different purposes. In simpler terms neural networks are algorithms.

There are a large number of training algorithms, each consisting of varied characteristics and performance capabilities, there could be used different algorithms to accomplish different goals.

Every day engineers develop many thousands of new algorithms. These new algorithms have the similar architecture to existing algorithms, and they are used to build real-world models. Here’s a guide to some of today’s common neural network algorithms. Read More