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Curated reading

Find the right ML article, read it, understand it, and apply it.

English reading guides for machine learning concepts with direct paths into visual, browser-based experiments.

20 articles found
Dimensionality Reduction

What Is PCA? Dimensionality Reduction You Can Try Interactively

Learn PCA from intuition to 2D/3D visualization, explained variance, and practical use cases.

12 min read · Visual guide Read article → Optimization

Gradient Descent Explained: A Visual and Intuitive Guide

Understand gradients, learning rate, batch variants, local minima, and parameter updates visually.

15 min read · Panduan optimasi Read article → Evaluation

Confusion Matrix and Classification Metrics

Read TP, FP, FN, TN, accuracy, precision, recall, specificity, F1, IoU, Dice, and Jaccard from a matrix.

15 min read · Metrics guide Read article → Deep Learning

Transformer and Attention Mechanism: From the Paper to an Interactive Lab

Explore Q, K, V, scaled dot-product attention, softmax, and attention matrices visually.

14 min read · Panduan attention Read article → Signal Processing

FFT: Turning Time Signals into Frequency Spectra

Understand how Fast Fourier Transform maps waveforms into frequency-domain information.

13 min read · Panduan sinyal Read article → Ensemble

Random Forest vs Decision Tree: Why Ensembles Often Win

Compare single-tree rules with ensemble voting, stability, variance reduction, and feature importance.

13 min read · Panduan ensemble Read article → Healthcare ML

Random Forest for Obesity Risk and Meal Planning

A practical healthcare ML case study using random forest for risk insight and meal planning.

16 min read · Studi kasus ML Read article → Clustering

K-Means Clustering: How Machines Group Unlabeled Data

Follow centroid assignment and update steps to understand unsupervised clustering.

11 min read · Panduan clustering Read article → Deep Learning

CNN Convolution Explained: From Pixels to Feature Maps

See kernels, sliding windows, activation, and feature maps in a compact CNN workflow.

13 min read · Panduan CNN Read article → Classification

SVM: Hyperplanes, Margins, and Kernel Trick Visually Explained

Build intuition for margins, support vectors, C regularization, and decision boundaries.

13 min read · Panduan SVM Read article → Signal Processing

Signal Decomposition: Separating Trend, Seasonality, and Residual

Learn how observed signals can be split into trend, seasonal pattern, and residual noise.

11 min read · Panduan time series Read article → ML Basics

Machine Learning for Beginners: From Data to Prediction

A beginner-friendly overview of data, features, models, training, evaluation, and prediction.

14 min read · Artikel Medium pilihan Read article → Workflow

Machine Learning Project Workflow: From Raw Dataset to Usable Model

Follow the practical steps from problem framing and cleaning to baseline, evaluation, and monitoring.

15 min read · Artikel Medium pilihan Read article → Roadmap

A Realistic Machine Learning Roadmap for Beginners

A practical path through Python, data, statistics, simple models, evaluation, projects, and deep learning.

13 min read · Artikel Medium pilihan Read article → ML Basics

Common Beginner Mistakes in Machine Learning and How to Avoid Them

Avoid skipping data understanding, baselines, evaluation, error analysis, and realistic projects.

12 min read · Artikel Medium pilihan Read article → Practical Guide

Choosing a Machine Learning Algorithm Without Getting Lost

Pick practical model candidates based on output type, data structure, constraints, and evaluation needs.

14 min read · Artikel Medium pilihan Read article → Paper Review

Paper Review: Attention Is All You Need

A guided review of the Transformer paper, attention, strengths, limitations, and how to read it.

10 min read · Paper review Read article → Paper Review

Paper Review: Random Forests

A guided review of Breiman’s random forest paper, ensemble strength, correlation, robustness, and variable importance.

10 min read · Paper review Read article → Paper Review

Paper Review: Support-Vector Networks

A guided review of the SVM paper, margin, kernel ideas, generalization, and practical limitations.

10 min read · Paper review Read article → Paper Review

Paper Review: Learning Representations by Back-propagating Errors

A guided review of the classic backpropagation paper and its role in neural network learning.

10 min read · Paper review Read article →