Python Module Index

o


	o
	`optiml`
	`optiml.ml`
	`optiml.ml.neural_network`
	`optiml.ml.neural_network.activations`
	`optiml.ml.neural_network.initializers`
	`optiml.ml.neural_network.layers`
	`optiml.ml.neural_network.losses`
	`optiml.ml.neural_network.regularizers`
	`optiml.ml.svm`
	`optiml.ml.svm.kernels`
	`optiml.ml.svm.losses`
	`optiml.ml.svm.smo`
	`optiml.ml.utils`
	`optiml.opti`
	`optiml.opti.constrained`
	`optiml.opti.constrained.active_set`
	`optiml.opti.constrained.frank_wolfe`
	`optiml.opti.constrained.interior_point`
	`optiml.opti.constrained.projected_gradient`
	`optiml.opti.unconstrained`
	`optiml.opti.unconstrained.line_search`
	`optiml.opti.unconstrained.line_search.conjugate_gradient`
	`optiml.opti.unconstrained.line_search.gradient_descent`
	`optiml.opti.unconstrained.line_search.line_search`
	`optiml.opti.unconstrained.line_search.newton`
	`optiml.opti.unconstrained.line_search.quasi_newton`
	`optiml.opti.unconstrained.proximal_bundle`
	`optiml.opti.unconstrained.stochastic`
	`optiml.opti.unconstrained.stochastic.adadelta`
	`optiml.opti.unconstrained.stochastic.adagrad`
	`optiml.opti.unconstrained.stochastic.adam`
	`optiml.opti.unconstrained.stochastic.adamax`
	`optiml.opti.unconstrained.stochastic.amsgrad`
	`optiml.opti.unconstrained.stochastic.gradient_descent`
	`optiml.opti.unconstrained.stochastic.rmsprop`
	`optiml.opti.unconstrained.stochastic.schedules`
	`optiml.opti.utils`