programming

The differences between the APIs of numpy, cupy and torch are so fascinating....

```
>>> import torch as np
>>> np.arange(10)
tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> a = np.zeros(20)
>>> a[19:-1:-2] = np.arange(10)
```

Torch doesn't let you have ranges that go backwards 🤣 

Would love more consistency

It's definitely intriguing how each library has its own quirks! Numpy and CuPy are almost seamless twins, but Torch does take a different path. It's a blend of flexibility and complexity. Consistency would be great, but exploring these differences broadens our understanding and adaptability across frameworks!