9 docs tagged with "unsupervised-learning"

Neural network autoencoders for unsupervised representation learning - undercomplete, denoising, sparse, contractive variants with PyTorch on MNIST, anomaly detection, and sparse autoencoders for LLM interpretability.

Master DBSCAN, OPTICS, HDBSCAN, and Mean Shift - density-based clustering algorithms that discover arbitrarily shaped clusters, handle varying densities, and identify anomalies without specifying the number of clusters.

The complete story of GANs - from Goodfellow's 2014 minimax formulation to DCGAN, Wasserstein GAN, Progressive GAN, and StyleGAN2 - including training instabilities, theoretical foundations, and why diffusion models eventually surpassed them.

Agglomerative and divisive hierarchical clustering - linkage criteria, dendrograms, cophenetic correlation, and production-scale strategies for discovering multi-scale data structure.

Master K-means clustering - Lloyd's algorithm convergence proof, K-means++ initialization with D² weighting, silhouette analysis, elbow method, Mini-batch K-means for large datasets, and customer segmentation pipelines.

Learn unsupervised learning algorithms - clustering, dimensionality reduction, and generative models - as applied in production ML systems.

Principal Component Analysis via eigendecomposition and SVD - covariance geometry, reconstruction error, Kernel PCA, Incremental PCA, whitening, and production use for preprocessing and anomaly detection.

Non-linear dimensionality reduction with t-SNE and UMAP - crowding problem, KL divergence optimization, perplexity, Barnes-Hut approximation, UMAP topological foundations, and production-safe usage.

Master Variational Autoencoders - ELBO derivation, reparameterization trick, β-VAE disentanglement, VQ-VAE discrete latent spaces, conditional VAE, and PyTorch implementation for MNIST generation and anomaly detection.