What is the role of an activation function in neural networks?

The role of an activation function in neural networks is to transform the weighted sum of inputs into an output signal. It introduces non-linearity into the model, allowing the network to learn complex patterns in the data. When a neural network processes input data, it computes a weighted sum of these inputs, which could theoretically be any real number. However, without an activation function, the output would also be just a linear combination of the inputs, limiting the model's capability to learn complex relationships.

By applying an activation function, such as sigmoid, tanh, or ReLU (Rectified Linear Unit), the network can produce outputs that are non-linear. This enables the learning of intricate functions, which is particularly important for tasks like classification, image recognition, and speech processing. The activation function essentially decides whether a neuron should be activated or not, contributing significantly to the performance and effectiveness of the neural network.

In contrast, storing data efficiently focuses on how data is managed and retrieved rather than on the model's output. Defining the architecture of the network relates to the structure and organization of layers and nodes within the model, but does not directly involve the transformation process per se. Analyzing data patterns is more about evaluating the inputs and outputs after training, rather