| Title | : | One Reason You May Struggle To Learn ML/AI |
| Lasting | : | 0.40 |
| Date of publication | : | |
| Views | : | 45 rb |
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Well looks like im screwed Comment from : @kylehewson6258 |
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Linear algebra and multivarient gradient descent : advanced math topicssorry bro, I was rolling on the ground laughing 😂😂😂 Comment from : @andrewhabroad |
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Honestly bro,brIf u passed, 12th grade maths,brIt's only at max 2-3 months of maths,brI'm saying 3 months , side by side, u can practice coding and u can make projects ❤ Comment from : @NasimaBegum-dg3ht |
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@techwithtim is AI ML hard for a beginner to understand or study Comment from : @Chaoticwhale4446 |
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linear algebra and gradient descent are high school level arent they? /gen, ofc most ppl wont have it in the back of their minds but a quick refresh course will remind you of the basics, calculus and algebra needed for ml is actually pretty simple Comment from : @ght0076 |
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Gradient descent involves very simple math Comment from : @Djellowman |
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Nah with AI in its current stay it's not hard Comment from : @ThisIsDan-i |
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learning ai yeah lmao Comment from : @raccoon2276 |
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Could u make Algo videos or python all DS Comment from : @adityanayak20 |
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Good info Comment from : @tictoc1433 |
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I wish someone had told me long ago, that being a passable software engineer didn't require expert math, I would've been more open to learning the science long ago Comment from : @travis8229 |
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One reason that struggling my ML journey is because my device didn't strong enough to run it 😅 Comment from : @ban_droid |
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Not really, the maths in ML are quite limited and simple, and most of it are 100 taken care by simple blackbox functions and tensor specialized HW The understanding, analytical side and innovative part is the hardest part in ML, the math part is trivial even compared to high school maths, and mostly skippable Comment from : @Songfugel |
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Of course brother every activities need focus and time Comment from : @shapiekhan |
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As a Data Scientist I can confirm that it can be very frustrating Unlike with software engineering, with ML you never know what you're gonna get Maybe you spend a ton of time coding, cleaning data, and engineering features only to find out that your data simply doesn't allow you to build a sufficiently accurate model In contrast, in software engineering, you'll always have something to show if you put in enough work Comment from : @datamonster3212 |
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