Training on large datasets with online learning

So far, we have trained our model on no more than 300,000 samples. If we go beyond this figure, memory might be overloaded since it holds too much data, and the program will crash. In this section, we will explore how to train on a large-scale dataset with online learning.

SGD evolves from gradient descent by sequentially updating the model with individual training samples one at a time, instead of the complete training set at once. We can scale up SGD further with online learning techniques. In online learning, new data for training is available in sequential order or in real time, as opposed to all at once in an offline learning environment. A relatively small chunk of data is loaded and preprocessed for training at a time, which releases the memory used to hold the entire large dataset. Besides better computational feasibility, online learning is also used because of its adaptability to cases where new data is generated in real...