When implementing algorithms with numerical libraries like ND4J, always prefer the most direct and type-safe API methods over older patterns. This improves code clarity, prevents subtle bugs, and often leads to better performance.

Key practices:

Use createFromArray instead of create for array initialization, as it has clear overloads for all primitive types: ```java

// Preferred: double arr_2d[][] = {{1.0,2.0,3.0},{4.0,5.0,6.0}}; INDArray x_2d = Nd4j.createFromArray(arr_2d);

// Avoid: double[] flat = ArrayUtil.flattenDoubleArray(myDoubleArray); int[] shape = {rows, cols}; INDArray myArr = Nd4j.create(flat, shape, ‘c’);

2. Prefer direct parameter methods over shape arrays for common operations:
```java
// Preferred:
INDArray x = Nd4j.zeros(DataType.DOUBLE, 5);

// Avoid:
int[] shape = {5};
INDArray x = Nd4j.zeros(shape, DataType.DOUBLE);

When handling arrays with different data types, explicitly use castTo for proper type conversion: ```java // When types don’t match INDArray x = Nd4j.zeros(5, DataType.DOUBLE); INDArray y = Nd4j.zeros(5, DataType.INT);

// Preferred: INDArray result = x.add(y.castTo(DataType.DOUBLE));

// Avoid - will throw exception: INDArray result = x.add(y); ```

Following these practices leads to more efficient algorithm implementations with fewer errors and better maintainability.

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