The answer
A hyperparameter that controls how much we adjust the model weights in response to the estimated error each time.
Understanding Backpropagation and Gradient Descent Simply
What is the learning rate?
More FAQs in Understanding Backpropagation and Gradient Descent Simply
The algorithm might overshoot the minimum error point and fail to converge, leading to unstable training.
When gradients become extremely small as they travel backwards, preventing early layers from updating their weights and learning.
A variation of gradient descent that updates model weights using only a single training example or mini-batch at a time, speeding up updates.
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