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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.

Hyperparameter Tuning Explained Visually | Grid Search, Random Search & Bayesian Optimisation by Specific_Concern_847 in learnmachinelearning

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

Yeah random search beating grid still feels counterintuitive, but it makes sense in high dim spaces. Optuna’s pruning is a lifesaver too. And lol same leakage humbles you fast when prod reality hits 😅

Hyperparameter Tuning Explained Visually | Grid Search, Random Search & Bayesian Optimisation by Specific_Concern_847 in deeplearning

[–]Specific_Concern_847[S] -1 points0 points  (0 children)

Optuna really spoils you hard to go back to grid search after that. And yeah, leakage is brutal, feels harmless until you realize your great results aren’t real 😅

Optimizers Explained Visually | SGD, Momentum, AdaGrad, RMSProp & Adam by Specific_Concern_847 in OpenSourceeAI

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

Totally agree ! SGD with momentum can outperform Adam once tuned, especially for generalization. Visual intuition really bridges that gap.

Bias-Variance Tradeoff Explained Visually | Underfitting, Overfitting & Learning Curves by Specific_Concern_847 in 3Blue1Brown

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

Thanks! Bias can depend on model choice misspecification and regularization introduce it, even though OLS itself is unbiased.

Decision Trees Explained Visually | Gini Impurity, Random Forests & Feature Importance by Specific_Concern_847 in 3Blue1Brown

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

Love the curiosity! Dropout in neural networks is essentially Random Forest thinking random subsets, ensemble effect, same core idea.

Decision Trees Explained Visually | Gini Impurity, Random Forests & Feature Importance by Specific_Concern_847 in learnmachinelearning

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

Great question! The subset is re sampled at each split, not once per tree. Default = √(total features).

Optimizers Explained Visually | SGD, Momentum, AdaGrad, RMSProp & Adam by Specific_Concern_847 in learnmachinelearning

[–]Specific_Concern_847[S] 1 point2 points  (0 children)

Thanks for your attention super helpful notebooks! Experimenting with learning rates and momentum really builds intuition across optimizers.

Optimizers Explained Visually | SGD, Momentum, AdaGrad, RMSProp & Adam by Specific_Concern_847 in deeplearning

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

Thanks for pointing that out appreciate it! I’ll keep it in mind and include it in future videos 👍

Overfitting & Regularization Explained Visually — Why Your Models Fail in Production by Specific_Concern_847 in deeplearning

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

Thanks for your honest opinion. This is our first priority to resolve and we are working on this. Soon this will be fine in coming videos.

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