摘要：Neural Network’s basic principles and functions are based on the nervous system of living organisms, they aim to simulate neurons of the human brain to solve complicated real-world problems by working in a forward-only manner. A recursive Neural Network on the other hand is based on a recursive design principle over a given sequence input, to come up with a scalar assessment of the structured input. This means that is ideal for a given sequence of input data that is when processed dependent on its previous input sequence, which by default are used in various problems of our era. A common example could be devices such as Amazon Alexa, which uses speech recognition i.e., given an audio input source that receives audio signals, tries to predict logical expressions extracted from its different audio segments to form complete sentences. But RNNs do not come with no problems or difficulties. Today’s problems become more and more complex involving parameters in big data form, therefore a need for bigger and deeper RNNs is being created. This paper aims to explore these problems and ways to reduce them while also providing a description of RNN’s beneficial nature and listing different uses of the state-of-the-art RNNs and their use in different problems as those mentioned above.
