Track 2 · Path · 5 explainers
Data & Features
Cleaning data, building features, embeddings, transfer learning, and dimensionality reduction
- 1
Data Preprocessing
Cleaning, scaling, and encoding raw data
- 2
Feature Extraction
How networks find patterns in raw data
- 3
Dimensionality Reduction
PCA and t-SNE — seeing high-dimensional data
- 4
Embeddings
Turning words and categories into vectors
- 5
Transfer Learning
Standing on the shoulders of pretrained giants