Changes Since Last Release

Encodings and neural networks in tiny-cuda-nn now share the same generic API for differentiable objects. This simplifies implementations significantly.
- As part of this generalization, encodings and neural networks can now take and produce row- and column-major matrices (i.e. both AoS and SoA data). Additionally, input data may be strided arbitrarily, which permits slicing of input matrices without copying.
Added GridEncoding support for double-backward, which is useful for e.g. eikonal supervision (courtesy of @ventusff).
Dropped the dependency on PyEXR / tinyexr in the sample applications (using imageio / stb_image instead).
Fixed many bug, added several performance improvements, and improved compatibility with older GPUs.

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