Neural Network Structure

Artificial Neural Networks are abbreviated as ANN. They fundamentally are computational models. ANNs are structured such that they emulate the functions of neurons present in the human brain. A human brain learns from the environment by connecting various neurons, whereas the neural networks learn from input and output data. On a fundamental level, ANNs behave as nonlinear statistical data such that there is a relationship between input and output. ANNs try to figure out different patterns using this relationship.

Similarity with Human Brain

ANNs are computational algorithms that are based on the inner workings of a biological nervous system. ANNs can use machine learning to recognize patterns, classify objects, and perform predictions. ANNs are composed up of interconnected layers and nodes. An ANN can also be categorized as an oriented or directed graph. Input layers first receive information, then the weights are assigned, and activation functions are used to process the information. ANNs have many interconnected processing blocks; these blocks are connected by nodes that are analogous to biological neurons. These nodes are present between each layer.

ANN Structure

ANNs contain artificial neurons. There can be any number of neurons, depending on the requirements of the application. These neurons are grouped into layers. The most commonly used ANN structure comprises an input layer, one or more hidden layers, and an output layer. Biological neurons communicate with each other by sending electric pulses, whereas ANNs pass the information across layers and nodes. At the nodes, the importance of the input signal is determined by associating weights. The value of weights can be positive or negative. A neuron is active if the weight is positive, whereas a neuron becomes inactive if the weight is negative. A neuron sums all input entries and multiplies these entries with the associated weight of the node.

ANN Layers

ANNs are arranged in layers. Nodes interconnect layers. Each node is characterized by a specific activation function. Typically, an ANN may have flowing layers.

Input layer

It receives the data, which usually is in the form of vectors. The vector may contain any number of parameters. Generally, the number of input nodes in the input layer is equal to the number of parameters in the input vector. Input layers preprocess the data and feed it to subsequent hidden layers. The input nodes do not change the data; it simply checks if the data is in a valid format and then passes it along to the next layer.

Hidden Layer

The main processing happens in the hidden layers. The number of hidden layers can vary. The incoming information passes through weighted connections in the hidden layer, where the input values are multiplied with weights. Subsequently, the weighted inputs are summed up to produce a single number.

Output layer

The processed information is directed towards the output layer. The output layer can be connected with the hidden layer, input layer, or both. In some cases, the output layers feed the information back to the input layer. The output layer generates the final prediction value. There is typically one output node in classification networks. The activation functions at the nodes of the output layer add and change the data to produce the output values. Proper weight adjustment is vital for neural networks to find useful data patterns and prevent overfitting.

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