Knowing the model simplifying process
In simpler words, simplifying a model concerns removing connections, neurons, or entire layers of the neural network to get a lighter model, i.e., a model with a reduced number of parameters. Naturally, the efficiency of the simplified version must be very close to the one achieved by the original model. Otherwise, simplifying the model does not make any sense.
To understand this topic, we must answer the following questions:
- Why simplify a model? (reason)
- How do we simplify a model? (process)
- When do we simplify a model? (moment)
We will go through each of these questions in the following sections to get an overall understanding of model simplification.
Note
Before moving on in this chapter, it is essential to say that model simplification is still an open research area. Consequently, some concepts and terms cited in this book may differ a little bit from other materials or how they are employed on frameworks and...
