> 2.2.1. Introduction
High-dimensional datasets can be very difficult to visualize. While data in two or three dimensions can be plotted to show the inherent structure of the data, equivalent high-dimensional plots are much less intuitive. To aid visualization of the structure of a dataset, the dimension must be reduced in some way.
[15:29:05] <ZA> [support]
> Tensors are similar to NumPy’s ndarrays, except that tensors can run on GPUs or other hardware accelerators. In fact, tensors and NumPy arrays can often share the same underlying memory, eliminating the need to copy data (see Bridge with NumPy). Tensors are also optimized for automatic differentiation (we’ll see more about that later in the Autograd section). If you’re familiar with ndarrays, you’ll be right at home with the Tensor API. If not, follow along!
[15:45:09] <ZA> [support]
↳ replying to ZA: "Tensors are similar to NumPy’s ndarrays, except th…"
> See Visualizing the stock market structure for an example of using manifold learning to map the stock market structure based on historical stock prices.