Optimization for Machine Learning

GPTKB entity

Predicate	Object
gptkbp:instanceOf	gptkb:academic
gptkbp:appliesTo	training algorithms
gptkbp:challenge	overfitting underfitting vanishing gradients local minima saddle points exploding gradients
gptkbp:goal	improve generalization maximize accuracy minimize loss function
gptkbp:hasApplication	autonomous vehicles computer vision natural language processing robotics speech recognition diagnosis recommendation systems financial modeling
gptkbp:hasMethod	gptkb:Newton's_method gptkb:Adam_optimizer gptkb:RMSprop gptkb:L-BFGS Bayesian optimization evolutionary algorithms coordinate descent batch optimization Adagrad Momentum optimization mini-batch optimization online optimization
https://www.w3.org/2000/01/rdf-schema#label	Optimization for Machine Learning
gptkbp:includes	gradient descent hyperparameter tuning stochastic gradient descent convex optimization non-convex optimization regularization techniques
gptkbp:relatedTo	gptkb:machine_learning mathematical optimization
gptkbp:studiedIn	applied mathematics computer science statistics
gptkbp:usedIn	gptkb:reinforcement_learning deep learning neural networks linear regression logistic regression support vector machines
gptkbp:bfsParent	gptkb:NeurIPS_2013
gptkbp:bfsLayer	6