Components of a deep learning system
Let us recall Yann LeCun’s definition of deep learning:
”Deep learning is a methodology: building a model by assembling parameterized modules into (possibly dynamic) graphs and optimizing it with gradient-based methods.”
The core idea here is that deep learning is an extremely modular system. Deep learning is not just one model but, rather, a language to express any model in terms of a few parametrized modules with these specific properties:
- It should be able to produce an output from a given input through a series of computations.
- If the desired output is given, it should be able to pass on information to its inputs on how to change, to arrive at the desired output. For instance, if the output is lower than what is desired, the module should be able to tell its inputs to change in some direction so that the output becomes closer to the desired one.
The more mathematically inclined...
