Artificial Neural Networks￼
Artificial neural networks are one of the most widely used machine learning algorithms today. It can be used in many areas, such as language processing or machine vision. It is used in many cases to interpret deep learning or large data sets and relationships.
Intuition behind neural networks
When we hear the word “neural,” the first thing that comes to mind is the neural network of the human brain. These networks are responsible for making people’s decisions. The biggest difference between the human neural network and the artificial neural network of robots is that the humans are able to learn on their own from the effects on their environment, as opposed to robots who follow and execute given instructions and do not develop themselves from implementations. In the case of machine learning, and in particular neural networks, artificial intelligence is able to develop itself in the learning phase, thereby achieving more accurate estimation efficiency. As the information travels through the neural network, the system detects the discrepancy between the expected and actual output data, thus changing its parameters. The more data that passes through the network, the more accurate the system provides. This is called the learning process.
There are two main elements that make up neural networks, these are called connections and nodes. Each relationship has an associated numeric weight that determines the sign and strength of the relationship. At the nodes, a mathematical operation takes place, similar to that in the human brain. The node combines the input of data from the previous connection with a coefficient or weight that strengthens or attenuates that input for the node. Thus, it is possible to determine the importance of each parameter for the learning algorithm, i.e., which inputs are most useful for estimating the error-free output for a given node.
The inputs to a node are summed and then processed using an activation function. The activation function helps determine whether or not a given signal travels on the network, thereby affecting the final output. neurons in the layers form the totality of the layers and the neural network. The original inputs arrive at the first layer properly weighted, where they advance to the next layers, activating certain neurons. This continues until we reach the output (there may be one or more). During the learning process, we use the teaching data to set the weights and activations in the network using the given inputs so that the difference between the expected and the actual output is as small as possible. The neural network is then ready to use the inputs to determine unknown outputs based on real or new data.