Structuring Your Problem

Compared to researchers, practitioners spend much less time determining which architecture to choose when starting a new deep learning project. Acquiring data that represents a given problem correctly is the most important factor to consider when developing these systems, followed by an understanding of the dataset's inherent biases and limitations. When starting to develop a deep learning system, consider the following questions for reflection:

• Do I have the right data? This is the hardest challenge when training a deep learning model. First, define your problem with mathematical rules. Use precise definitions and organize the problem into either categories (classification problems) or a continuous scale (regression problems). Now, how can you collect data pertaining to those metrics?
• Do I have enough data? Typically, deep learning algorithms have shown to perform much better on large datasets than on smaller ones. Knowing how much data is...