Neural networks are a concept that tries to make a computer model of the human brain. This system tries to perform various computational tasks faster than traditional systems. A neural network consists of a circuit of neurons. In a modern sense, it can be seen as an artificial neural network that is made up of artificial neurons or nodes. In other words, we can say that a neural network is either an artificial neural network that can be used to solve AI problems or a biological neural network made up of real biological neurons. Students learning neural networks must have basic knowledge of algorithms, mathematics, and programming. However, we know that for one reason or another, students struggle with assignments in this field.
In technology, we can define neural networks as a set of algorithms modelled loosely after the human brain. Neural networks are designed to recognize and classify patterns, and approximate, cluster, and optimize data.
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An artificial neural network’s central theme is borrowed from the analogy of biological neural networks. These systems are also called parallel distributed processing systems or connectionist systems. An ANN is made up of a collection of units that are interconnected in a particular pattern. The connection allows the units to communicate with each other. The units, sometimes referred to as neurons or nodes are processors operating in parallel.
A connection link is used to connect the neurons to each other. Each of these connection links is associated with a weight. The weight has essential information about the input signal, which is the most useful information that neurons need to solve a particular problem. This is because the weight inhibits the signal that is being communicated. An activation signal is the internal state of each neuron. Combining the input signals and activation rule produces output signals which may be sent to other units.
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Like we mentioned in the previous section, the concept of neural networks was borrowed from how biological neurons behave. Thus, we must discuss this to help you understand ANN. From your basic biology class, you probably learned that a neuron is a special cell that processes information. The human body has approximately 1011 neurons with 1015 Interconnections.
A neuron has four parts that play a significant role in how it works:
· Dendrites – They look like tree branches and are responsible for receiving information from other neurons that it is connected to. In simple words, we can say that they are like the ears of the neuron.
· Soma – This is the body of the cell. It processes information received from the dendrites.
· Axon – This is the cable through which information is sent by neurons
· Synapses - Other neuron dendrites and the axon are connected using synapses.
There are several similarities between a biological neuron and an artificial neural network:
· Soma – Node
· Dendrites – input
· Synapse – interconnections or weights
· Axon – output
There are also some differences between the two. We are going to discuss the differences based on some of the criteria mentioned:
A biological neuron is massively parallel, slow but superior to the artificial neural network. On the other hand, ANN is massively parallel, fast but inferior to a biological neuron.
A biological neuron tolerates ambiguity while artificial network neurons are very precise. They require formatted and structured data to tolerate ambiguity.
· Tolerance to fault
In a biological neuron, even partial damage will degrade performance. On the other hand, an artificial neuron network is capable of robust performance. It has the potential of being fault-tolerant.
· Storage capacity
Information in biological neurons is stored in the synapses while the ANN stores information in continuous memory locations.
Artificial neural networks rely on the following three building blocks to process information. Our neural network's homework helpers have discussed them in detail below.
· Network Topology
The arrangement of the nodes along with connecting lines to form a network is called network topology. Artificial neural networks can be classified into the following topologies:
This is a non-recurrent network. It has processing nodes in layers. The nodes in each layer are connected with the nodes from the previous layers. The signal can flow in one direction, from input to output because there is no feedback loop. A feedforward network can be divided into the following:
· A single layer feedforward network
· Multilayer feedforward network
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This type of network topology has feedback paths. This means that using loops, the signal can flow in both directions. It is a non-linear dynamic system that continuously changes until a state of equilibrium is reached. The following are the types of feedback network:
· Recurrent networks
· Fully recurrent networks
· Jordan networks
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· Adjustment of weights or learning
In an artificial neural network, learning refers to the method used to modify the weights of connections between the neurons of a specified network. We can classify learning in the following three categories:
Just like the name suggests, this type of learning is dependent. An input vector is presented to the network which in turn generates an output vector. The received output vector is then compared with the expected or desired output. If there is a difference between the output vector and the desired output, an error signal will be generated. Based on this error signal, the weights will be adjusted until they match the desired output.
This type of learning process is independent. The input vectors of a similar type are combined to form clusters. The neural network provides an output response when a new input pattern is applied. The response indicates the class to which the input pattern belongs.
The environment does not relay feedback as to what should be the desired output and if it is correct or not. It is up to the network itself to discover the patterns and features from the input data, and the relation for the input data over the output.
This learning reinforces or strengthens the network over some critical information. Although it is quite similar to supervised learning, we might have less information. The network under reinforcement learning receives some feedback from the environment.
Please note that the feedback here is not instructive but rather evaluative. The network then performs adjustment of the weights to get better critical information in the future after receiving the feedback.
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An activation function is an extra force or effort that is applied over the input to obtain an exact output. These functions can also be applied in ANN over the input to get the exact output. Some of the activation functions of interest include:
Also known as the identity function, a linear activation function does not perform input editing.
There are two types of sigmoid activation function:
· Binary sigmoidal function
This type does input editing between 0 and 1. Its output cannot be less than 0 and more than 1 because it is always bounded and positive in nature. Also, a binary sigmoidal function is increasing in nature.
· Bipolar sigmoidal function
It carries out input editing between -1 and 1. Its output cannot be less than -1 or more than 1. Like the sigmoid function, it is also increasing in nature.
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