Continuing the prior post, we are moving on to Retail. Woo Hoo. As i stated in the prior blog; This came out of at Fast.ai ppt that can be found here. Granted they only provided the list.

This post will be made of a lot of quotes and references, that is kind of the point, very little original content will come from me as I am not the creator of much data science, just a user of it, though i am sure i will add commentary especially in retail as i have some practical experience in a few areas.

The funny thing about solving a data science problem is that their are many ways to solve it, so i don’t expect this to be 100% comprehensive, i try to find what appears to be a canonical solution, though that does not mean you cannot stuff everything into a neural net and close your eyes, which is what everyone appears to be doing these days…

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Though some of these are actually optimization…

This came out of at Fast.ai ppt that can be found here. Granted they only provided the list. You will notice an ethics deck they have uploaded as well, I encourage you to review at it. I have a few ethics slides in my data science talk, but the fast.ai gang hit way harder than I typically do. I admit, shock is a good way to wake people into thinking about what they are doing.

In their ML Applications deck they have a list of applications by industry, below I have them listed out and what I hope is to present either an elevator pitch of what each one is, or and executive overview of each and links to more info. This post will be made of a lot of quotes and references, that is kind of the point, no original content will come from me as I am not the creator of much data science, just a user of it, though i am sure i will add commentary. This will be a series of blogs posts, and clearly each post has the potential for being very long even with just a brief summary and a few links.

The funny thing about solving a data science problem is that their are many ways to solve it, so i don’t expect this to be 100% comprehensive, i try to find what appears to be a canonical solution, though that does not mean you cannot stuff everything into a neural net and close your eyes, which is what everyone appears to be doing these days…

Enjoy

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