A neural network, also known as an artificial neural network (ANN), is a branch of machine learning models inspired by the principles of neuronal organization discovered in biological neural networks constituting animal brains
. Neural networks are a subset of machine learning and are at the heart of deep learning algorithms
. They consist of interconnected nodes or neurons in a layered structure that resembles the human brain
. Each connection, or synapse, in a neural network can transmit a signal to other neurons, and each neuron processes the incoming signals to produce an output
. Key components of neural networks include:
- Artificial Neurons : These are software modules that mimic the behavior of biological neurons. They are connected to each other through edges, which transmit signals and have associated weights
- Layers : Neural networks typically have an input layer, one or more hidden layers, and an output layer. Each layer extracts and processes different features or data
- Training : Neural networks learn by processing examples, each containing a known input and result. The network adjusts its weights to minimize the error between the calculated outputs and the given target values
Neural networks are important because they can make intelligent decisions with limited human assistance, as they can learn and model the relationships between input and output data that are nonlinear and complex
. They have been used in various applications, such as image recognition, natural language processing, and predictive analytics. There are different types of neural networks, including feedforward neural networks (FNNs), which have a single layer of output nodes and are the simplest type of neural network
. Deep learning algorithms, on the other hand, typically consist of networks with more than three layers, including the input and output layers
