Especially with deep learning, where you have many layers full of nodes, it's hard to understand the "thinking" of a network because you'll have to reverse-engineer million of float values and try to make sense of them.
The ability to reuse the knowledge of one model and adapt it to solve a different problem is one of the most consequential breakthroughs in machine learning.
Grab your ☕️ and let's talk about this.
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A deep learning model is like a Lego set, with many pieces connected, forming a long structure.
These pieces are layers, and each layer has a responsibility.
Although we don't know exactly the role of every layer, we know that the closer they get to the output, the more specific they get.
The best way to understand what I mean is through an example: a model that will process car images.