I was reading a book on Deep Learning when I came across a line, more like a few words that didn't make apparent sense.
Thus, we will often settle for sampling a random minibatch of examples every time we need to compute the update, a variant called minibatch stochastic gradient descent. In each iteration, we first randomly sample a minibatch B consisting of a fixed number of training examples. We then compute the derivative (gradient) of the average loss on the minibatch with regard to the model parameters. Finally, we multiply the gradient by a predetermined positive value η and subtract the resulting term from the current parameter values.
We can express the update mathematically as follows ($∂$ denotes the partial derivative):
$(w,b) \leftarrow (w,b) - {η\over |B|} {\Large \Sigma}_{I \in B} \partial_{(w,b)}l^{(i)}(w,b).$
The set cardinality $|B|$ represents the number of examples in each minibatch (the batch size) and $η$ denotes the learning rate.
What does "current" parameter values mean in this context?
You can find the book here -
https://d2l.ai $\rightarrow$ Chapter Linear Neural Networks, Part 3.1.1.4