Add README and comments for MNIST autoencoder example

Author: NA-V10

dl4j-examples/src/main/java/org/deeplearning4j/examples/quickstart/modeling/feedforward/unsupervised/MNISTAutoencoder.java

@@ -17,6 +17,11 @@
  * SPDX-License-Identifier: Apache-2.0
  ******************************************************************************/
 
+
+  // MNISTAutoencoder.java
+ // Demonstrates training an autoencoder on MNIST digit images.
+// Autoencoders learn compressed representations (unsupervised learning).
+
 package org.deeplearning4j.examples.quickstart.modeling.feedforward.unsupervised;
 
 import org.apache.commons.lang3.tuple.ImmutablePair;
@@ -81,6 +86,8 @@ public static void main(String[] args) throws Exception {
                         .build())
                 .build();
 
+                // Build a simple autoencoder: Encoder → Bottleneck → Decoder
+
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.setListeners(Collections.singletonList(new ScoreIterationListener(10)));
 
@@ -101,6 +108,8 @@ public static void main(String[] args) throws Exception {
             INDArray indexes = Nd4j.argMax(dsTest.getLabels(),1); //Convert from one-hot representation -> index
             labelsTest.add(indexes);
         }
+         
+        // Train the autoencoder to minimize reconstruction loss
 
         //Train model:
         int nEpochs = 3;
@@ -154,8 +163,10 @@ public int compare(Pair<Double, INDArray> o1, Pair<Double, INDArray> o2) {
                 worst.add(list.get(list.size()-j-1).getRight());
             }
         }
+ 
+         //Visualize by default
+        // Evaluate reconstruction quality or print sample reconstructions
 
-        //Visualize by default
         if (visualize) {
             //Visualize the best and worst digits
             MNISTVisualizer bestVisualizer = new MNISTVisualizer(2.0, best, "Best (Low Rec. Error)");

dl4j-examples/src/main/java/org/deeplearning4j/examples/quickstart/modeling/feedforward/unsupervised/README.md

@@ -0,0 +1,43 @@
+# Unsupervised Learning Examples – Autoencoder (DeepLearning4J)
+
+This folder contains unsupervised learning examples implemented using DeepLearning4J.  
+The primary example in this directory demonstrates how to train an autoencoder on MNIST digits to perform dimensionality reduction and reconstruction.
+
+---
+
+## 🧠 MNISTAutoencoder.java
+
+A simple autoencoder trained on the MNIST dataset (28×28 grayscale digit images).  
+Autoencoders learn to compress input data into a lower-dimensional representation and then reconstruct it.
+
+### What this example shows
+- How autoencoders work
+- How to compress images into a bottleneck latent space
+- How to reconstruct input images
+- How unsupervised neural networks are trained
+
+### Key Concepts
+- **Encoder:** Compresses image → latent representation  
+- **Decoder:** Reconstructs latent representation → image  
+- **Loss Function:** Measures reconstruction quality  
+
+### Expected Behavior
+The autoencoder gradually learns to:
+- Rebuild digit outlines
+- Capture key features
+- Reduce noise
+
+This is not a classifier — it learns **patterns** without labels.
+
+---
+
+## ✔ How to Run
+
+mvn -q exec:java -Dexec.mainClass="org.deeplearning4j.examples.quickstart.modeling.feedforward.unsupervised.MNISTAutoencoder"
+
+
+---
+
+## 🙌 Why This README Helps
+The unsupervised folder previously had no explanation, run instructions, or conceptual overview.  
+This documentation improves clarity and helps beginners understand autoencoders and unsupervised learning techniques in DL4J.