What is the typical range for the small positive value of a learning rate?

The typical range for a small positive value of a learning rate is focused on ensuring effective convergence during the training of machine learning models. A learning rate that is too high can cause the model to diverge, while too low a rate can result in excessively slow training or getting stuck in local minima.

While the learning rate can technically be set in the range from 0.0 to 1.0, that encompasses a very broad scale without a focus on what is deemed effective for most machine learning applications. The upper end of that range may lead to instability in training. In practice, values usually trend towards the lower end for effective learning.

For typical use, smaller learning rates like those found in ranges such as 0.01 to 0.1 or even lower are often used. However, saying that the learning rate can range from 0.0 to 1.0 mistakenly implies that such a large value would be effective in all cases, which is not the norm in real-world scenarios. Therefore, while B captures a wide range, it does not specifically identify the small values commonly used in practice for effective learning without overwhelming the optimization process.