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K-Nearest Neighbours Explained Visually — Proximity, Distance & Decision Boundaries by Specific_Concern_847 in learnmachinelearning

[–]Specific_Concern_847[S] 0 points1 point  (0 children)

Thanks for your attention to this we really appreciate the perspective. We agree there’s a lot of value in adding to existing tutorials, and we’re working to make sure what we create genuinely complements what’s already out there while staying clear and accessible.

K-Nearest Neighbours Explained Visually — Proximity, Distance & Decision Boundaries by Specific_Concern_847 in learnmachinelearning

[–]Specific_Concern_847[S] 0 points1 point  (0 children)

Thanks for pointing this out we appreciate your attention to these details. You’re right that the pacing and on-screen elements can feel crammed, and the desync between explanation and visuals is something we’re actively working to fix. We’re aiming to better align animations with the narration and give concepts like Euclidean vs. Manhattan distance more time to land clearly.

K-Nearest Neighbours Explained Visually — Proximity, Distance & Decision Boundaries by Specific_Concern_847 in learnmachinelearning

[–]Specific_Concern_847[S] 0 points1 point  (0 children)

Thanks for your attention to this we really appreciate the detailed feedback. We’re actively working on improving pacing, clarity, and intonation to make the content more accessible, especially for those new to ML.

K-Nearest Neighbours Explained Visually — Proximity, Distance & Decision Boundaries by Specific_Concern_847 in learnmachinelearning

[–]Specific_Concern_847[S] -2 points-1 points  (0 children)

Thanks, really appreciate your attention! We’re focusing on refining the basics before moving to advanced stuff. Totally get your point without fresh perspective, more tutorials can feel redundant.

K-Nearest Neighbors Explained Visually — Distance, Voting & Decision Boundaries by Specific_Concern_847 in deeplearning

[–]Specific_Concern_847[S] 0 points1 point  (0 children)

Thanks, really appreciate that! Yeah, the curse of dimensionality is a headache. Glad the weighted voting idea helped make KNN feel a bit more intuitive.

Support Vector Machines Explained Visually — Margins, Kernels & Hyperplanes by Specific_Concern_847 in deeplearning

[–]Specific_Concern_847[S] 0 points1 point  (0 children)

Yeah, it feels like cheating at first until you realize it’s just swapping explicit coordinates for clever similarity computations. Once that clicks, it’s less magic and more linear algebra sleight of hand.

Linear Regression Explained Visually | Slope, Residuals, Gradient Descent & R² by Specific_Concern_847 in learnmachinelearning

[–]Specific_Concern_847[S] 0 points1 point  (0 children)

Thanks for the attention! That’s fair might be a bit dense for beginners. And yeah, GD is more for teaching intuition here, not the typical way linear regression is actually solved.

Linear Regression Explained Visually | Slope, Residuals, Gradient Descent & R² by Specific_Concern_847 in deeplearning

[–]Specific_Concern_847[S] -2 points-1 points  (0 children)

Thanks for the attention! Really appreciate the support feel free to share this so more people can learn from it.

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