Neural network architecture refers to the structure and design of artificial neural networks, which are computer systems inspired by the human brain. At its core, a neural network consists of layers of interconnected nodes, or neurons, that process data. These layers include an input layer (where data enters), one or more hidden layers (where the data is processed), and an output layer (where the final result is produced). Each connection between neurons has a weight that adjusts as the network learns, allowing it to improve its predictions over time. The architecture can vary in complexity, from simple networks with a few layers to deep networks with many layers, known as deep learning. This flexibility enables neural networks to tackle a wide range of tasks, such as image recognition, language translation, and even playing games. Overall, neural network architecture is crucial for building systems that can learn and adapt to new information.
