MACHINE LEARNING

Detailed explanation-1: -A learning rate that is too large can cause the model to converge too quickly to a suboptimal solution, whereas a learning rate that is too small can cause the process to get stuck.

Detailed explanation-2: -A too high learning rate will make the learning jump over minima but a too low learning rate will either take too long to converge or get stuck in an undesirable local minimum.

Detailed explanation-3: -If your learning rate is set too low, training will progress very slowly as you are making very tiny updates to the weights in your network. However, if your learning rate is set too high, it can cause undesirable divergent behavior in your loss function.

Detailed explanation-4: -Learning Rate: This is the hyperparameter that determines the steps the gradient descent algorithm takes. Gradient Descent is too sensitive to the learning rate. If it is too big, the algorithm may bypass the local minimum and overshoot.

Detailed explanation-5: -Reducing the learning rate improves the likelihood of convergence, but requires more iterations to find the optimal solution (local, if not global). In conclusion, the pace of learning influences the convergence rate, but is not overfitting.