Do not expect readability, stability, compatibility, portability, applicability, or survivability. Most of them are just a verbatim dump of what I'm using on my system(s). Simply sharing them to give ...
Running ML code on jupyter notebooks is good for quick prototyping and model exploration. On production level large scale model training settings we should prefer .py ML pipelines packed into a docker ...
Every chapter in this book flows in some way or another. Tutorials have a corresponding explanation chapter. An explanation chapter will discuss the point of a tutorial and explain the topics that ...
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