Next on the agenda is getting my head around adding some kind of lightweight micropub endpoint to write to my Hugo markdown repo and trigger a rebuild. For now though, the Malazan book of the fallen is calling<\/p>",
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"content": " @jamesravey<\/span><\/a><\/span> Ooh, and you do NLP! I've been trying to get into it for a hobby project, and I'm fascinated with what I've learned so far.<\/p> Any tips for someone dipping his toes into the deep ML pond for the first time?<\/p>",
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"content": " @benjaminhollon<\/span><\/a><\/span> sure - I'd say as tempting as $LatestShinyThing may be, start with the fundamentals - the NLTK book + python library is pretty old school but great foundational material (https:\/\/www.<\/span>nltk.org\/book\/<\/span><\/span><\/a>) whereas the spacy library + 101 course (https:\/\/<\/span>course.spacy.io\/en\/<\/span><\/span><\/a>) is a good \"I don't care about the theory, I just want to build something cool\" alternative. - Maybe I should write about these during my 100 days!<\/p>\nNLTK Book<\/a>",
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"content": " @jamesravey<\/span><\/a><\/span> I love this post! I always put so much pressure on myself to start off with a clean once January comes. But like anything, taking small steps is probably better in the long run compared to just going \"cold turkey\".<\/p>",
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"content": " @edel<\/span><\/a><\/span> agreed! I find it's so much easier to justify just giving up to yourself if you try to make a lot of changes at once and they don't stick. Much prefer to introduce one \"better\" thing a week and make sure the habits bed in before I try and change the next thing!<\/p>",
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"content": " @jamesravey<\/span><\/a><\/span> You are lucky. Since I use AndOTP for my OTP app, I get more text messages from spammers and telco's ads.<\/p> Note: I notice you use Google Analytics. It is good and well. But may be you want to consider to move to more privacy oriented analytics like @plausible<\/span><\/a><\/span><\/p>",
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"content": " @jamesravey<\/span><\/a><\/span> chaotic systems! when i was doing speech rec training we would regularly get mandates from the boss or the customer to \"improve the frontier graph\" by x points or whatever and they'd grill us on what we thought would give that improvement. about I dunno like 50% of the time the result of executing that plan was a degradation of model performance. (this is why I left that gig too, the catch-22 of improving already-optimized chaotic systems was too much)<\/p>",
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"content": " @capn_b<\/span><\/a><\/span> ah that old chestnut! I guess it's almost impossible to avoid overfitting when you're already performing well on general use cases<\/p>",
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"url": "https:\/\/twitter.com\/afcaber"
},
"content": "Interesting debugging from James. Beware the reproducibility of your deep learning models on different machines with different floating point hardware.\n<\/a>",
"published": "2022-01-25T09:54:12+00:00"
}
}
]
}