About Neural Networks

neural network

Neural Networks

The neural network is an interconnection of artificial neurons to form a circuit or network. It's either an artificial neural network meant to solve queries related to artificial intelligence or a biological neuron. The main application area of the artificial network is in predicting models, adapting control and in those domains wherein data sets can train them. The weights in biological neurons are used to indicate connections, a negative weight means inhibitory connections. The inputs are summed up but before this step, they undergo modification by weight like taking average values, on the other hand, a positive weight means an excitatory connection. The overall process is called a linear combination and the range value of output is controlled by an activation function. The former one has self-learning ability which enables it to learn from past experiences that takes place within the network, this helps in retrieving data and information from complex data sets too.

More about Neural Networks

The neural network was formed back in 1944 by researchers namely Walter pits and Warren McCullough. It then became one of the most researched topics in computer science and neuroscience Fields for almost four decades.

Deep learning is an approach to neural networking, it is the reason behind the most popular feature of Google that is an automatic translator and smartphone speech recognizers. The concept of machine learning is not a new term in this field machine learning involves a mechanism by which the system learns from data sets provides, experiences, past mistakes. It uses such things to perform an activity in a smarter way in future and ensure no mistakes and neural networks help in performing ML so that systems can perform efficiently.

The neural network is an algorithm that helps in identifying the interrelationships amongst data sets and resources through a series of processes like the human brain does, neutral networks give unique and best output available according to the varying input conditions and don't need processing again. It consists of millions of processing nodes connected to each other via networks in the same way as brain nerve fibres of humans are present.

The mechanism that neural network follows is: it assigns a serial number in the form of weights to each incoming connections. On network activation node gets the data set which is a new number, this process continues for each node and multiplies it with the corresponding weight. Thus, it then results in a single numbered output. Now, based on logical conditioning such as," if the data is greater than the threshold value the node fires "or "if the data point is below threshold value node passes no data to next layer." The neural network during trial periods has all its weights and threshold data calibrated to certain random values. The same mechanism is followed and output is obtained for given input values. Now, input values are varied for correction and changes until the accurate output is obtained i.e., to ensure the same output is obtained on repeating similar conditions.

Some of the areas where neural network finds its application are:

They are mostly used in financial activity, planning, data analytics and predicting the fate and also in malpractice detection and security assessments.

The neural network estimates price and cost data to unveil opportunities related to trade decisions which are based on the analysis it also helps in taking out dependent and independent factors.

It predicts the results and future outcomes based on past experience and data.

They also help in the processing of data with the help of clustering, separating and compression of signals.

This feature of Neural network along with regression and association analysis makes it possible to automatically separate spam mails to a different folder.

A challenge that neural network faces is the learning experience it requires to serve the system in future in a better way. It costs a diverse and huge amount of data and information, circumstances, situation and past experiences to ensure its proper functioning in future.

The future objective of neural networking field is to build up biological neural systems to understand the working of biological systems. Recent researches involve progress in the domain of neuromodulation, like the determination of the role of dopamine in learning. The neural network is growing up collaborations like BCM theory to build up by physical models and with such growing aspects it's moving ahead.