👉 Activation engineering is a design strategy in artificial neural networks that focuses on optimizing the activation functions used in neurons to improve the network's performance and learning efficiency. Traditional activation functions, such as the sigmoid or hyperbolic tangent, can suffer from issues like vanishing gradients, which hinder the network's ability to learn complex patterns. Activation engineering seeks to address these limitations by introducing new activation functions or modifying existing ones to better capture the underlying data distribution, enhance gradient flow, and improve the overall dynamics of the network. This can involve designing functions with more favorable properties, such as non-linearity that adapts to the data's complexity, or incorporating mechanisms like smooth activation curves to facilitate smoother gradient updates. By fine-tuning these activation functions, engineers aim to create more robust and efficient neural networks capable of tackling challenging tasks with greater accuracy and speed.
