What are Neural Networks?
Neural networks are a subset of machine learning and are at the core of deep learning algorithms. They are inspired by the biological neural networks that constitute animal brains. A neural network consists of interconnected layers of nodes, or "neurons," which work together to process input data and produce output.
Each neuron receives input data, applies a weight to it, and passes it through an activation function to determine its output. The layers are typically categorized as input layers, hidden layers, and output layers. The input layer receives the raw data, hidden layers process this data through complex calculations, and the output layer produces the final result.
Neural networks excel at recognizing patterns and making predictions, making them highly effective for a variety of tasks, including image and speech recognition, natural language processing, and game playing. They learn from large datasets and improve their accuracy through a process known as backpropagation, where the network adjusts its weights based on the errors in its predictions.
In conclusion, neural networks are powerful tools in artificial intelligence that mimic the way human brains process information, enabling machines to learn and make informed decisions autonomously.