class_docstr: AdaDelta optimizer.
  
  Parameters
  ----------
  rho
  eps
  
  Attributes
  ----------
  g2 : collections.defaultdict
  s2 : collections.defaultdict
  
  Examples
  --------
  
  >>> from river import datasets
  >>> from river import evaluate
  >>> from river import linear_model
  >>> from river import metrics
  >>> from river import optim
  >>> from river import preprocessing
  
  >>> dataset = datasets.Phishing()
  >>> optimizer = optim.AdaDelta()
  >>> model = (
  ...     preprocessing.StandardScaler() |
  ...     linear_model.LogisticRegression(optimizer)
  ... )
  >>> metric = metrics.F1()
  
  >>> evaluate.progressive_val_score(dataset, model, metric)
  F1: 80.56%
  
  References
  ----------
  [^1]: [Zeiler, M.D., 2012. Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701.](https://arxiv.org/pdf/1212.5701.pdf)
code: def __init__(self, rho=0.95, eps=1e-8):
      super().__init__(lr=None)
      self.rho = rho
      self.eps = eps
      self.g2 = collections.defaultdict(float)
      self.s2 = collections.defaultdict(float)
  
file_path: river/river/optim/ada_delta.py
imports: from __future__ import annotations
  import collections
  from river import optim
label_desc: Use image node_id 1 to create a new AdaDelta object from inherited base classes: optim.base with example: obj = AdaDelta(rho, eps)