Monte-Carlo methods

decomposition of a Hermitian, positive-definite matrix into the product of a lower triangular matrix and its conjugate transpose. (used for Monte-Carlo simulations) A = LL^{*} where L is a lower triangular matrix with real and positive diagonal entries, and L^{*} is the conjugate transpose of L.

Detects pattern within data and use it to make useful prediction. Generally AI \subset ML \subset DL Some main exploration: Transformers Large language models NLP CNN Logistic regression Optimization gradient descent hyperparameter tuning ensemble learning Recommender systems Reinforcement learning Q-learning Policy Gradient Monte-Carlo Tree Search Generative Models GAN VAE Autoencoder sparse autoencoder sparse crosscoders Supervised Q-learning Low-rank adapters Fields mechanistic interpretability Related: linear algebra.

structured generations in vLLM a la carte

Economics for engineer, a guide.