In machine learning:
Convergence occurs when the optimization process, such as training a model using gradient descent, steadily progresses towards a minimum point in the loss function. It means the model’s parameters are adjusting in such a way that the loss (or error) is being minimized with each iteration, indicating the learning process is successful and stable.
Divergence, on the other hand, happens when the optimization process doesn’t progress towards a minimum. Instead, the loss might fluctuate significantly or even increase, indicating instability in the learning process. Divergence can occur due to several factors, such as a learning rate that’s too high or a poorly chosen model architecture.
See: Back propagation
In networked thought:
Convergence occurs where different ideas or information streams come together, leading to a unified understanding or solution. It’s akin to collaborative problem-solving, where diverse perspectives merge to enhance decision-making accuracy or innovation.
Divergence involves branching out from a central idea, encouraging creativity and exploration of multiple possibilities or viewpoints. Divergence supports brainstorming and generating a wide range of ideas before narrowing them down for further development.
See: Networked thought