A Primer on Deep Learning

In a post-competition interview competition's winners noted the value of focusing on feature generation, also called feature engineering. Data scientists spend a significant portion of their time, effort, and creativity working on engineering good features; in contrast, they spend relatively little time running machine learning algorithms. A simple example of an engineered feature would involve subtracting two columns and including this new number as an additional descriptor of your data. In the case of the whales, the winning team represented each sound clip in its spectrogram form and built features based on how well the spectrogram matched some example templates. After that, they then subsequently iterated new features that would help them correctly classify examples that they got wrong through the use of a previous set of features.