Artificial neural network (ANN)
Artificial Neural Networks (ANNs) are computational models inspired by the structure and functioning of the human brain. They consist of layers of interconnected nodes, or “neurons,” that process data by passing it through activation functions and weighted connections. Introduced in the 1940s by Warren McCulloch and Walter Pitts, the concept of ANNs laid the foundation for modern machine learning and AI. Over the decades, advancements like backpropagation, introduced in 1986, revolutionized ANNs by enabling efficient training of deep networks for tasks such as image recognition and natural language processing.
https://en.wikipedia.org/wiki/Artificial_neural_network
The architecture of an ANN typically includes an input layer, one or more hidden layers, and an output layer. Each layer processes data and passes it to the next, with hidden layers learning abstract representations of the input. Activation functions like ReLU and sigmoid introduce non-linearities, allowing ANNs to model complex relationships in data. TensorFlow, introduced in 2015, and PyTorch, introduced in 2016, are widely used frameworks for building and training ANNs due to their flexibility and scalability in handling large datasets and complex models.
https://en.wikipedia.org/wiki/Deep_learning
ANNs have been successfully applied in diverse fields such as autonomous vehicles, healthcare diagnostics, and fraud detection. For instance, convolutional neural networks (CNNs), a specialized form of ANN, excel in image-related tasks, while recurrent neural networks (RNNs) are suited for sequential data processing. The integration of ANNs with cloud computing and specialized hardware like GPUs and TPUs has further accelerated their adoption. As research advances, ANNs continue to evolve, playing a central role in driving innovation across industries.