What is the architecture of a typical neural network?

The architecture of a typical neural network is best described as consisting of an input layer, one or more hidden layers, and an output layer. This structure is fundamental to how neural networks operate, as it allows them to process and learn from data effectively.

The input layer is responsible for receiving the initial data, which is then fed into one or more hidden layers. These hidden layers are crucial because they perform various computations and transformations on the input data, effectively enabling the network to learn complex patterns and relationships. Each neuron in these layers applies a weighted sum of the inputs followed by an activation function, enabling non-linear decision boundaries.

Finally, the output layer produces the result of the network's computations, which could be classification labels, regression values, or any other type of output depending on the specific application of the neural network. This layered approach, with the ability to have multiple hidden layers, is what gives deep neural networks their capacity to learn intricate features from large datasets.

In contrast, architectures with just an input and an output layer lack the capability to model complex relationships, making them less effective for many tasks, especially those involving high-dimensional data. Similarly, architectures with multiple input and output layers but no hidden layers would not leverage the power of intermediate processing, limiting