PyTorch Tensor Basics

Now that we know WTF a tensor is, and saw how Numpy's ndarray can be used to represent them, let's switch gears and see how they are represented in PyTorch. PyTorch has made an impressive dent on the machine learning scene since Facebook open-sourced it in early 2017. It may not have the widespread adoption that TensorFlow has -- which was initially released well over a year prior, enjoys the backing of Google, and had the luxury of establishing itself as the gold standard as a new wave of neural networking tools was being ushered in -- but the attention that PyTorch receives in the research community especially is quite real. Much of this attention comes both from its relationship to Torch proper, and its dynamic computation graph. As excited as I have recently been by turning my own attention to PyTorch, this is not really a PyTorch tutorial; it's more of an introduction to PyTorch's Tensor class, which is reasonably analogous to Numpy's ndarray.