Looks like some great resources. Thank you. One thing I'm struggling with since I've started paying attention to all the ML, deep learning stuff is that ML and AI are used interchangeably.
For every problem, it seems like, the first reaction is to wonder how to use ML to solve it.
It may be the cynic in me but I feel like few years ago every conversation or solution to a problem would lead very quickly to 'Big data'
I think where I'm getting at is (finally!) What resources are there to help me clearly understand the scope of ML and AI and how to develop my thinking to objectively identify problems where they provide a good solution
Side note: there was recently a post which made it to HN front page about how deep learning isn't the best solution for a small dataset and a counter post later about how it is..
Agreed. There is definitely a hype cycle on this (I have a school textbook for "data mining", which was also a precursor) What I like about these talks is they are primarily case studies of projects, so you can see how the team broke down a problem, as opposed to focusing on specific, tactical algorithms or techniques.
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Looks like some great resources. Thank you. One thing I'm struggling with since I've started paying attention to all the ML, deep learning stuff is that ML and AI are used interchangeably.
For every problem, it seems like, the first reaction is to wonder how to use ML to solve it.
It may be the cynic in me but I feel like few years ago every conversation or solution to a problem would lead very quickly to 'Big data'
I think where I'm getting at is (finally!) What resources are there to help me clearly understand the scope of ML and AI and how to develop my thinking to objectively identify problems where they provide a good solution
Side note: there was recently a post which made it to HN front page about how deep learning isn't the best solution for a small dataset and a counter post later about how it is..
Agreed. There is definitely a hype cycle on this (I have a school textbook for "data mining", which was also a precursor) What I like about these talks is they are primarily case studies of projects, so you can see how the team broke down a problem, as opposed to focusing on specific, tactical algorithms or techniques.