Stochastic Gradient Descent System

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A Stochastic Gradient Descent System is an numeric optimization system that implements an SGD algorithm to solve an SGD task.

  • AKA: SGD Solver.
  • Example(s):
    • scikit-learn sdg[1]
      • from sklearn.linear_model import SGDClassifier ;
        X = [[0., 0.], [1., 1.]] ;
        y = [0, 1] ;
        clf = SGDClassifier(loss="hinge", penalty="l2") ;
        clf.fit(X, y) ;
        SGDClassifier(alpha=0.0001, class_weight=None, eta0=0.0, fit_intercept=True, learning_rate='optimal', loss='hinge', n_iter=5, n_jobs=1, penalty='l2', power_t=0.5, rho=0.85, seed=0, shuffle=False, verbose=0, warm_start=False)
      • from sklearn.linear_model import SGDRegressor ; ...
    • scikit-learn SGDRegressor[2]
  • See: Gradient Descent System, CNTK.


References

2015