AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

The activation function is essential in neural networks because it introduces non-linearity into the model, which allows the network to learn and represent complex patterns in the data. Without non-linearity, a neural network could be reduced to a linear model, regardless of how many layers it contains. This would severely limit the network's ability to solve more complicated problems, such as image and speech recognition, where relationships within the data are inherently non-linear.

For instance, using simple linear functions would mean that no matter how many layers are built into the network, the output could always be represented as a linear combination of inputs. This would drastically reduce the model's expressive power, as many real-world relationships cannot be adequately described by linear functions alone. By employing non-linear activation functions, such as ReLU, sigmoid, or tanh, neural networks can learn intricate mappings from inputs to outputs, thereby enhancing their performance on a variety of tasks.

The other choices, while related to some aspects of neural network functions, do not capture the core function of activation functions. For example, converting categorical data to numerical is the role of encoding techniques, ensuring linearity of the model is contrary to the purpose of activation functions, and initializing weights pertains to how model training begins, rather than