Github user njayaram2 commented on a diff in the pull request:
https://github.com/apache/incubator-madlib/pull/149#discussion_r128065248
--- Diff: src/ports/postgres/modules/convex/mlp.sql_in ---
@@ -0,0 +1,752 @@
+/* -----------------------------------------------------------------------
*//**
+ *
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ *
+ *
+ * @file mlp.sql_in
+ *
+ * @brief SQL functions for multilayer perceptron
+ * @date June 2012
+ *
+ *
+ *//*
----------------------------------------------------------------------- */
+
+m4_include(`SQLCommon.m4')
+
+/**
+@addtogroup grp_mlp
+
+<div class="toc"><b>Contents</b><ul>
+<li class="level1"><a href="#mlp_classification">Classification</a></li>
+<li class="level1"><a href="#mlp_regression">Regression</a></li>
+<li class="level1"><a href="#optimization_params">Optimizer
Parameters</a></li>
+<li class="level1"><a href="#predict">Prediction Functions/a></li>
+<li class="level1"><a href="#example">Examples</a></li>
+<li class="level1"><a href="#background">Technical Background</a></li>
+<li class="level1"><a href="#literature">Literature</a></li>
+<li class="level1"><a href="#related">Related Topics</a></li>
+</ul></div>
+
+Multilayer Perceptron (MLP) is a model for regression and
+classification
+
+Also called "vanilla neural networks", they consist of several
+fully connected hidden layers with non-linear activation
+functions. In the case of classification, the final layer of the
+neural net has as many nodes as classes, and the output of the
+neural net can be interpreted as the probability of a given input
+feature belonging to a specific class.
+
+
+@brief Solves classification and regression problems with several
+fully connected layers and nonlinear activation functions.
+
+@anchor mlp_classification
+@par Classification Training Function
+The mlp classification training function has the following format:
+<pre class="syntax">
+mlp_classification(
+ source_table,
+ output_table,
+ independent_varname,
+ dependent_varname,
+ hidden_layer_sizes,
+ optimizer_params,
+ activation
+ )
+</pre>
+\b Arguments
+<DL class="arglist">
+ <DT>source_table</DT>
+ <DD>TEXT. Name of the table containing the training data.</DD>
+
+ <DT>model_table</DT>
--- End diff --
It is `model_table` here but `output_table` above.
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