An artificial neural network (ANN) is a type of machine learning model that is built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. ANNs are a subset of machine learning and are at the heart of deep learning algorithms. They are designed to mimic the function and structure of the human brain and are made up of interconnected nodes or neurons in a layered structure. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. ANNs are based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals, processes them, and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs.
ANNs are used to solve complicated problems, like summarizing documents or recognizing faces, with greater accuracy. They can learn and model the relationships between input and output data that are nonlinear and complex, making them useful in tasks such as speech recognition or image recognition. ANNs are trained using a training set, and once they are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity.
Some advantages of ANNs include observation-based decisions, where the ANN can learn from events and make decisions based on the observations, and unorganized data processing, where ANNs are exceptionally good at organizing large amounts of unstructured data. ANNs have found applications in many disciplines, including data processing, robotics, and social media analysis.
