A Brief Introduction to Neural Networks

1. A Brief Introduction to A Brief Introduction to Neural Networks Neural Networks \

2. Dr. Robert Hecht- Nielsen defines neural network as ‘a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs. These neural networks are usually organized in layers and layers are made up of interconnected nodes which contain an activation function. The patterns are available to the network via the ‘input layer’, which further conveys to one of more hidden layers where the actual processing is done via system of weighted connections. These hidden layers then link to an output layer where the answer is output. Most of the artificial neural networks comprise of some form of ‘learning rule’ which modified the weights of the connections according to the input patterns that it is presented with. Once a simple neural network is trained to a satisfactory level, it may be used as an analytical tool on other data. Moreover, it is also possible to over-train a neural network, which means that the network has been trained exactly to respond to only one type of input, which is much like mechanical memorization. Applications Neural Networks should be used for: Neural networks are like universal approximates, and they work best if the system you are using them to model has a high tolerance to error. However, they work very well for: Finding associations or discovering regularities within a set of patterns Relationships between variables are not understood

3. Relationships are difficult to describe sufficiently with conventional methods. How does a Neural Network Learn Things? Information flows through a neural networking tow ways. When it is learning or being trained, patterns of information are processed into the network via the input units, which trigger the layers of hidden units, and these in turn arrive at the output units. And this common design is called a feed forward network. Each unit gets inputs from the units to its left, and the inputs are multiplied by the weights of the connections they travel along. Each unit adds up all the inputs it receives in its way and if the sum is more than a particular threshold value, the unit fires and triggers the units it is connected to. To conclude, neural networks have made computer systems more useful by making them more of a human. So the next time you think you might like your brain to be as reliable as a computer, think again- and be grateful that you have access to such an amazing neural network already installed in your mind!