Machine Learning

25 Most Important Mathematical Definitions in Data Science

...in a single frame.

Apr 25 •

8 Fatal (Yet Non-obvious) Pitfalls in Data Science

...and how to identify and handle them.

Apr 23 •

Intrinsic Measures for Clustering Evaluation

Three reliable methods for clustering evaluation.

Apr 22 •

11 Key Probability Distributions in Data Science

Essential for statistical modeling.

Apr 20 •

Train Classical ML Models on Large Datasets

Extending the Bagging objective.

Apr 18 •

How To Avoid Getting Misled by t-SNE Projections?

Some key and lesser-known observations from t-SNE results.

Apr 17 •

5 Must-Know Ways to Test ML Models in Production

...with implementation.

Apr 14 •

Train and Test-time Data Augmentation

More data from existing data.

Apr 12 •

and

Condense Random Forest into a Decision Tree

Preserve generalization power while reducing run-time.

Apr 9 •

Interactively Prune a Decision Tree

Prune a decision tree in seconds with a Sankey diagram.

Apr 7 •

A Beginner-friendly Guide to Multi-GPU Training

Learn how to scale models using distributed training.

Apr 6 •

What is Bhattacharyya Distance?

...And how it differs from KL divergence.

Apr 5 •

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