Working with bigger data – online algorithms and out-of-core learning
If you executed the code examples in the previous section, you may have noticed that it could be computationally quite expensive to construct the feature vectors for the 50,000-movie review dataset during grid search. In many real-world applications, it is not uncommon to work with even larger datasets that can exceed our computer's memory. Since not everyone has access to supercomputer facilities, we will now apply a technique called out-of-core learning, which allows us to work with such large datasets by fitting the classifier incrementally on smaller batches of a dataset.
Text classification with recurrent neural networks
In Chapter 16, Modeling Sequential Data Using Recurrent Neural Networks, we will revisit this dataset and train a deep learning-based classifier (a recurrent neural network) to classify the reviews in the IMDb movie review dataset. This neural network-based classifier...
