Update tutorials/deeplearning/README.md

h2o · h2o · commit 46f8a2423f30 · 2015-11-06T17:23:19.000-08:00
diff --git a/tutorials/deeplearning/README.md b/tutorials/deeplearning/README.md
@@ -506,7 +506,7 @@ L1 and L2 penalties can be applied by specifying the `l1` and `l2` parameters. I
 By default, Deep Learning training stops when the `stopping_metric` does not improve by at least `stopping_tolerance` (0.01 means 1% improvement) for `stopping_rounds` consecutive scoring events on the training (or validation) data. By default, `overwrite_with_best_model` is enabled and the model returned after training for the specified number of epochs (or after stopping early due to convergence) is the model that has the best training set error (according to the metric specified by `stopping_metric`), or, if a validation set is provided, the lowest validation set error. Note that the training or validation set errors can be based on a subset of the training or validation data, depending on the values for `score_validation_samples` or `score_training_samples`, see below. For early stopping on a predefined error rate on the *training data* (accuracy for classification or MSE for regression), specify `classification_stop` or `regression_stop`.
 
 ####Training Samples per MapReduce Iteration
-The parameter `train_samples_per_iteration` matters especially in multi-node operation. It controls the number of rows trained on for each MapReduce iteration. Depending on the value selected, one MapReduce pass can sample observations, and multiple such passes are needed to train for one epoch. All H2O compute nodes then communicate to agree on the best model coefficients (weights/biases) so far, and the model may then be scored (controlled by other parameters below). The default value of `-2` indicates auto-tuning, which attemps to keep the communication overhead at 5% of the total runtime. The parameter `target_ratio_comm_to_comp` controls this ratio. This parameter is explained in more detail in the [H2O Deep Learning booklet](http://h2o.ai/resources/),
+The parameter `train_samples_per_iteration` matters especially in multi-node operation. It controls the number of rows trained on for each MapReduce iteration. Depending on the value selected, one MapReduce pass can sample observations, and multiple such passes are needed to train for one epoch. All H2O compute nodes then communicate to agree on the best model coefficients (weights/biases) so far, and the model may then be scored (controlled by other parameters below). The default value of `-2` indicates auto-tuning, which attemps to keep the communication overhead at 5% of the total runtime. The parameter `target_ratio_comm_to_comp` controls this ratio. This parameter is explained in more detail in the [H2O Deep Learning booklet](http://h2o.ai/resources/).
 
 ####Categorical Data
 For categorical data, a feature with K factor levels is automatically one-hot encoded (horizontalized) into K-1 input neurons. Hence, the input neuron layer can grow substantially for datasets with high factor counts. In these cases, it might make sense to reduce the number of hidden neurons in the first hidden layer, such that large numbers of factor levels can be handled. In the limit of 1 neuron in the first hidden layer, the resulting model is similar to logistic regression with stochastic gradient descent, except that for classification problems, there's still a softmax output layer, and that the activation function is not necessarily a sigmoid (`Tanh`). If variable importances are computed, it is recommended to turn on `use_all_factor_levels` (K input neurons for K levels). The experimental option `max_categorical_features` uses feature hashing to reduce the number of input neurons via the hash trick at the expense of hash collisions and reduced accuracy. Another way to reduce the dimensionality of the (categorical) features is to use `h2o.glrm()`, we refer to the GLRM tutorial for more details.
