Minibatch is a collection of examples that are fed into the network, (example after example), and back-prop is done after every single example. We then take average of these gradients and update our weights. This completes processing 1 minibatch.

Because of that, we can run several minibatches in parallel, granted we have enough memory for temporary storage.

I read these posts 

 - [\[1\]][1] 
 - [\[2\]][2],
 - [about padding entries in a minibatch so they have same length][3] 
 - and [about preserving the cell state][4] but the following is still unclear to me:

**Question part a:**

How a minibatch entity would look like for LSTM? 
Say, I want it to reproduce Shakespeare, letter by letter (30 characters to choose from). 

I launch LSTM, let it predict for 200 characters of a poem, then perform back propagation. (hence, my LSTM works with 200 timesteps). Does this mean my minibatch consist of 1 example whose length is 200?

**Question part b:**

If I wanted to launch 63 other minibatches in parallel, would I just pick 63 extra poems? 

**Question part C:**

If I wanted each minibatch to consist of 10 different examples, what would such examples be, and how would they be different from 'what I perceive as a minibatch'?


  [1]: https://stats.stackexchange.com/questions/245799/lstm-how-to-feed-the-network-with-a-mini-batch-when-to-reset-the-lstm-state
  [2]: https://groups.google.com/forum/#!topic/theano-users/chsxlScGJIk
  [3]: https://stats.stackexchange.com/a/245951/187816
  [4]: https://www.reddit.com/r/MachineLearning/comments/4egr3v/in_rnnlstm_why_passing_the_states_across/