I want to create a neural network and train it on some data, however I want to be able to create a new model without retraining it from the start.
An example, I have 1000 data points in my training data
- model - trained on 0-99
- model - trained on 1-100
- model - trained on 2-101
- and so forth
So I'm wondering if I can use the first model to train the second model, essentially forgetting the first data point.
You can view it as a sliding window over the 1000 data points, sliding one data point to the right for each new model.
Does it make sense? Is there any easy way to solve this problem?