Artificial Neural Network (ANN)
Computational model inspired by the human brain, composed of nodes (neurons) organized in layers that process information to solve complex problems.
Key Concepts
Neurons
The basic building blocks of an ANN.
Layers
ANNs are organized into layers of neurons.
Weights
The connections between neurons have weights, which are adjusted during training.
Activation Functions
Used to introduce non-linearity into the network.
Detailed Explanation
An artificial neural network (ANN) is a computational model that is inspired by the structure and function of the brain. ANNs are composed of a large number of interconnected processing elements (neurons) that work in unison to solve specific problems.
ANNs are trained by a process of trial and error. During training, the weights of the connections between neurons are adjusted so that the network learns to produce the desired output for a given input.
ANNs are used in a wide variety of applications, including image recognition, natural language processing, and machine translation.
Real-World Examples & Use Cases
Image Recognition
ANNs are used to recognize objects in images. For example, they are used in self-driving cars to identify pedestrians and other vehicles.
Natural Language Processing
ANNs are used to understand and generate human language. For example, they are used in machine translation to translate text from one language to another.
Machine Translation
ANNs are used to translate text from one language to another. For example, Google Translate is a machine translation service that is powered by ANNs.