message: Update README and move old release notes to release notes page
diff: @@ -17,7 +17,7 @@ to be able to overcome the many limitations of fussy, brittle and tightly-couple
  GPU-based software architectures. By bridging the gap between the universal
  mathematical descriptions of deep learning operations, such as convolution, and
  the platform and chip-specific operations that enable acceleration, PlaidML makes
  -deep learning work
  +deep learning work everywhere.
  As a component under `Keras`_ PlaidML can accelerate training workloads with
  customized or automatically-generated Tile code. It works especially well on
  