Deep Learning Essentials

The representation viewpoint

For most traditional machine learning algorithms, their performance depends heavily on the representation of the data they are given. Therefore, domain prior knowledge, feature engineering, and feature selection are critical to the performance of the output. But hand-crafted features lack the flexibility of applying to different scenarios or application areas. Also, they are not data-driven and cannot adapt to new data or information comes in. In the past, it has been noticed that a lot of AI tasks could be solved by using a simple machine learning algorithm on the condition that the right set of features for the task are extracted or designed. For example, an estimate of the size of a speaker’s vocal tract is ...