Machine Learning Guide
A machine learning podcast teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models, neural networks, math, languages, frameworks, etc. Where visual ML resources provide the trees, I provide the forest. Podcasts are a great supplement during exercise, commute, chores, etc.
MLA 011 Practical Clustering
Nov 07, 2020
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Kmeans (sklearn vs FAISS), finding n_clusters via inertia/silhouette, Agglomorative, DBSCAN/HDBSCAN
Try a walking desk to stay healthy while you study or work!
Show Notes
Clustering via Kmeans, Agglomorative, or DBSCAN/HDBSCAN.
- Kmeans. sklearn.cluster.KMeans, Faiss.kmeans.
- Finding optimal number of clusters n_clusters. kmeans.inertia_ inside a loop, pass results to kneed.KneeLocator (Gnothi sample code). Or silhouette_score (preferred).
- Agglomorative clustering with pre-computed cosine similarity square matrix (Gnothi sample code)
- DBSCAN / HDBSCAN