Improve and expand README documentation for NDArray examples

nd4j-ndarray-examples/README.md

@@ -1,3 +1,5 @@
+
+
 ## Eclipse Deeplearning4j: ND4J NDArray Examples
 
 This project contains a set of examples that demonstrate how to manipulate NDArrays. The functionality of ND4J demonstrated here can be likened to NumPy.
@@ -48,3 +50,114 @@ Create and fit a normalizer, and save and restore it.
 * [CustomOpsExamples.java](./src/main/java/org/nd4j/examples/advanced/operations/CustomOpsExamples.java)
 **Only relevant to the 1.0.0-beta6 release**. There are some operations that were implemented in C++ that had not been mapped to Java. This example demonstrates how to access them using ND4J's DynamicCustomOp. As of the beta7 release all maps have corresponding Java mappings.
 
+# ND4J NDArray Examples
+
+This module contains simple examples that demonstrate how to use **ND4J**  
+(N-dimensional arrays for Java) — the core numerical computing library behind Deeplearning4j.
+
+ND4J provides fast tensor operations similar to NumPy, backed by optimized BLAS  
+implementations.
+
+These examples help new users understand:
+
+- What an `INDArray` is  
+- How to create arrays  
+- How to perform basic math  
+- How to reshape, slice, and combine arrays  
+- How to run the examples using Maven
+
+---
+
+## 📌 What is an NDArray?
+
+An **NDArray** (N-dimensional array) is ND4J’s fundamental data structure.  
+It represents:
+
+- Scalars (0D)
+- Vectors (1D)
+- Matrices (2D)
+- Higher dimensional tensors (3D, 4D, …)
+
+ND4J operations are *vectorized* and run efficiently on CPU or GPU (via CUDA backend).
+
+Example:
+
+```java
+INDArray arr = Nd4j.create(new float[]{1, 2, 3});
+System.out.println(arr);   // [1.00, 2.00, 3.00]
+📘 Basic NDArray Operations
+Create Arrays
+java
+
+INDArray zeros = Nd4j.zeros(3, 3);
+INDArray ones = Nd4j.ones(2, 2);
+INDArray random = Nd4j.rand(1, 5);
+INDArray arange = Nd4j.arange(1, 10);
+Math Operations
+java
+
+INDArray a = Nd4j.create(new float[]{1, 2, 3});
+INDArray b = Nd4j.create(new float[]{4, 5, 6});
+
+System.out.println(a.add(b));       // elementwise add  
+System.out.println(a.mul(b));       // elementwise multiply
+System.out.println(a.mmul(b.T()));  // matrix multiplication
+Reshape and Shape Ops
+
+INDArray m = Nd4j.linspace(1, 9, 9).reshape(3, 3);
+System.out.println(m);
+
+System.out.println(m.transpose());
+System.out.println(m.permute(1, 0));
+▶️ How to Run These Examples
+Make sure you have:
+
+Java 8 or later
+
+Maven installed
+
+Then run:
+
+
+mvn -q exec:java -Dexec.mainClass="org.nd4j.examples.quickstart.BasicNDArrayExample"
+Or run any other example under:
+
+
+
+src/main/java/org/nd4j/examples/
+Example:
+
+
+mvn -q exec:java -Dexec.mainClass="org.nd4j.examples.advanced.SlicingExample"
+📤 Expected Output (Sample)
+Running a basic NDArray creation example:
+
+
+Copy code
+Zeros array:
+[[0.00, 0.00, 0.00],
+ [0.00, 0.00, 0.00],
+ [0.00, 0.00, 0.00]]
+
+Random array:
+[0.42, 0.91, 0.12, 0.55, 0.33]
+
+Vector add:
+[5.00, 7.00, 9.00]
+📁 Project Structure
+
+nd4j-ndarray-examples/
+ ├── src/
+ │   └── main/java/org/nd4j/examples/
+ │        ├── advanced/
+ │        ├── quickstart/
+ │        ├── utils/
+ │        └── resources/
+ ├── pom.xml
+ └── README.md
+📄 License
+This project is part of the Eclipse Deeplearning4j examples collection
+and is licensed under the Apache License 2.0.
+
+Happy coding with ND4J tensors! 🚀
+