Chapter 10. Recurrent neural networks
- Understanding the components of a recurrent neural network
- Designing a predictive model of time-series data
- Using the time-series predictor on real-world data
Back in school, I remember my sigh of relief when one of my midterm exams consisted of only true-or-false questions. I can’t be the only one who assumed that half the answers would be True and the other half would be False.
I figured out answers to most of the questions and left the rest to random guessing. But that guessing was based on something clever, a strategy that you might have employed as well. After counting my number of True answers, I realized that a disproportionate number of False answers were lacking. So, a majority of my guesses were False to balance the distribution.
It worked. I sure felt sly in the moment. What exactly is this feeling of craftiness that makes us feel so confident in our decisions, and how can we give a neural network the same power?
One answer is to use context to answer questions. Contextual cues are important signals that can improve the performance of machine-learning algorithms. For example, imagine you want to examine an English sentence and tag the part of speech of each word.