Understanding the Input Parameters in RNN
I'm having a hard time to understand the different "jargons" used in RNN. They are the following:
batch_size, time_steps, inputs and instances.

Let me go through my understanding of each input parameters &amp; please correct me where I'm wrong.
Suppose I've got a sequence of numbers and I want to predict the next number. The numbers are the following:
[1,2,3,4,5,....,100]

time_steps: This parameter means how far RNN will look into past before it predicts the future. For simplicity, I want to predict 1 number ahead. And want to do after I see 10 numbers in the past. So, in this case, time_steps will be 10.

inputs: These are the values at each time_steps. In first time_step (t) the inputs are
t0: [1]

t1: [2]

.
.
.


t10: [10]`

batch_size: This helps in efficient computation of RNN model. Suppose my batch_size is 2. In that case, at time_step 2, the RNN input will be

t0: [1]
t0: [11]


Then what's the usage of instances? E.g. in this post, instances have been used. And there are multiple cases where instances are used. Is it means each loop over batch? E.g. there are 5 batches, each of size 2. Then there will be 5 instances.

Please help me correct my understanding.

Thanks!

Alright pal, you did good learning those concepts. I had a hard time learning those correctly. Everything you know seems to be in order and as for "instances". They're basically a set of data. There's no fixed term of usage of "instances" in a deep learning community. Some people use it for referring for a different set of data or batches of data. I rarely hear it in papers. 
