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A Software Engineer’s Explanation of Deep Learning Models
Non-data scientists often use the word Model to mistakenly refer to the what in Machine Learning we canonically call Model Architecture. And it couldn't be more wrong!
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The Model Architecture is very important to the Model. Without the variety of architectures we have we would never be able to fit data for the vastness of different problems that are currently being successfully solved by Deep Learning.
However, the Weights are a vital part of the model too. Different Weights can describe functions that have absolutely different geometry.
Model = Architecture (a.k.a. algorithm) + Weights (a.k.a. parameters)
Let's have a look at how it works:
