Difference - Batch Training or training multiple times? I have a question on batch learning of neural network.
A neural network learns in batches and modifies weights in every iteration. Question: If I save checkpoints after a batch, and then load the weights at a later time and train with new batch, will it be different from training both batches in 1 go?  

Example: If I have a batch size of 100 and training data of 1000 points. So would it be different, in outputted checkpoint file, if I train with 9 batches (900 data points) in one go -> save checkpoint -> load checkpoint next day -> train with last batch -> save checkpoint ... Vs give all 1000 datapoints (i.e., 10 batches) -> train -> save checkpoint file ?
There will not be any difference between :


Training all batches + Epoch in one go 
Saving a checkpoint and resuming the training later 


Saving a checkpoint is a very good practice. It is not uncommon for training to fail due to environmental issues (E.g.: Spot instance on AWS gets kicked out , PC crash). 

With Libraries like Keras, it is very straight-forward to checkpoint models.