Your language models dont take into account how he visually displays his items. His scripts are careful not to be biased. However he USES an iphone. He wears an apple watch in his android videos. He leaves the android phones with dust on them while the apple phones are flawless. There's alot more to it than that. Subtle differences like light balance. Filming location. Facial expressions and thumb nail previews give off an air of sceptism or mistrust. It can induce a fear response in anyone thats sensitive to these characteristics. Maybe this phone isnt durable. Maybe others will think less of me for having this phone because he's looking at it with an fake smile. Try your model again accounting for all the variables and try not to cherry pick them.
What I love about the class imbalance problem in NLP is that you can easily generate synthetic data to make up the difference. That's harder to do with numerical datasets because you don't know how playing around with numbers can mess things up.
Really great video! I'm building an app for transcribing daily reflections for self awareness and id love to implement something similar to this video - breaking down your transcript into different life pillars like health career relationships etc, then categorise the semantic analysis, and create a life wheel to visualize balance
Of re punctuate was more accurate o would pick that an run on my local machine for days 😂 ,if I was doing for a job and there may I’d pick the second option
You did good today UA-cam algo, you did good. Subbed.
I appreciate it!
Huge data science fan here!! Love your channel man ❤ it is a good day when one of your vids comes out
Your language models dont take into account how he visually displays his items.
His scripts are careful not to be biased.
However he USES an iphone. He wears an apple watch in his android videos.
He leaves the android phones with dust on them while the apple phones are flawless.
There's alot more to it than that. Subtle differences like light balance. Filming location. Facial expressions and thumb nail previews give off an air of sceptism or mistrust. It can induce a fear response in anyone thats sensitive to these characteristics. Maybe this phone isnt durable. Maybe others will think less of me for having this phone because he's looking at it with an fake smile.
Try your model again accounting for all the variables and try not to cherry pick them.
bro said 🤓
edit: great content, I subbed!
🤓☝️ the data indicates
What I love about the class imbalance problem in NLP is that you can easily generate synthetic data to make up the difference. That's harder to do with numerical datasets because you don't know how playing around with numbers can mess things up.
100%, one caveate i have anecdotally found is if the synthetic NLP data goes over ~30% the accuracy begins to decline
@@datanash8200 Ooooh, I'll take note and be on the lookout for that 👍
This is the best video of the channel so far IMHO. I really love how you go into making the full project
Really great video! I'm building an app for transcribing daily reflections for self awareness and id love to implement something similar to this video - breaking down your transcript into different life pillars like health career relationships etc, then categorise the semantic analysis, and create a life wheel to visualize balance
That sounds interesting, can't wait to see it!
love the video and channel
Thank you man
@@datanash8200 also i am biased and am saying that mkbhd is not biased :)
loved the video ♥! the quality seems a bit visual low eventho its 1080p. youtube compression ... 🤦♂
Sorry about that
that was awesome
Thanks!
That was a very good and fun demonstration of a data science project and a great video, thanks 🎉
Glad you enjoyed it!
Of re punctuate was more accurate o would pick that an run on my local machine for days 😂 ,if I was doing for a job and there may I’d pick the second option
😂😂 the pain of waiting was too much
HELL YEAAAA
Thank you man
very good work. i am impressed.
Thank you! Cheers!
Definitely Biased…
Of course he's biased. Big Western UA-camrs especially are. Americans in particular. Come on now.
Are you football made simple? You have the same voice
No but i have heard that a few times 😅
the tableau link in the description isnt valid btw ♥
Thanks mate, all fixed!!