@chipro This is awesome. I have also put together a short practical guide on production level deep learning, with the focus on the tech stack. Any feedback would be appreciated :)https://github.com/alirezadir/Production-Level-Deep-Learning …
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This is a cool summary. Love exercises part. Ex#26 - i would make the model that extract angles from the image and then use extracted data to classify image into one of three classes - triangle, circle or a square.
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@chipro Great contribution! I wrote something similar last year, perhaps it will be useful to you:http://bit.ly/quaesita_dmguide …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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It look very concise to me, easy to digest, and educative.
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Great resource!
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this is amazing! love the research vs. production section. the exercises are also focused on how one would think and dissect the problem vs. which neural net one would use etc.
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This is absolutely fantastic. Thank you so much!
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another input: as a practitioner, I always struggle to be up to date. A big value add would be a table / list of frameworks for each of your building blocks. E.g. alternatives to mlflow, hypteropt? Out-of-the box solutions? Splunk Enterprise, Azure ML, Google AutoML
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Thank you for putting this together. It is amazing and extremely useful. I love the exercises section.
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