Artificial Neural Networks (ANNs) are computational models inspired by the biological neural networks found in the human brain. They are composed of interconnected artificial neurons or nodes, which process information and learn from input data. ANNs are capable of recognizing patterns, making decisions, and learning from experience, much like the human brain.
ANNs consist of layers: an input layer, one or more hidden layers, and an output layer. Each layer contains multiple nodes, and the connections between nodes have associated weights that are adjusted during the learning process to improve the network's performance.
For example, in image recognition tasks, an ANN can learn to identify specific objects by analyzing various features of the image, such as edges, shapes, and colors. Through repeated exposure to images and corresponding labels, the network adjusts its weights to minimize prediction errors.
In the context of cloud computing, ANNs can be utilized to build scalable and efficient machine learning applications. Platforms like Tencent Cloud offer services that simplify the deployment and management of neural network models, enabling users to leverage powerful computational resources for tasks like natural language processing, image recognition, and predictive analytics without the need for extensive hardware investments.