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Dimensionality Reduction
Techniques to reduce the number of input features while preserving important information (PCA, t-SNE, autoencoders).
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4 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Feature ✓ understood
A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.
- Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
- Unsupervised Learning ✓ understood
Learning from unlabeled data to discover hidden patterns, structures, or relationships without explicit target outputs.
- Dimensionality Reduction · you are here ✓ understood
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Dimensionality Reduction
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Foundations Principal Component Analysis A dimensionality reduction technique that transforms data into orthogonal components ordered by variance explained. Foundations Feature Selection Choosing the most relevant features from available data to reduce dimensionality and improve model performance. Foundations Curse of Dimensionality Phenomena where algorithms become inefficient as dimensionality increases, including data sparsity and distance concentration. Foundations Manifold Hypothesis The assumption that high-dimensional data lies on or near a lower-dimensional manifold, justifying dimensionality reduction. Neural Networks Autoencoder An unsupervised neural network that learns to compress data into a latent representation and reconstruct it, useful for dimensionality reduction. Neural Networks Latent Space A compressed, learned representation space where similar data points are close together, used in autoencoders and VAEs.