message: PR review notes updated
diff: PlaidML is an advanced and portable tensor compiler for enabling deep learning
  -on laptops, embedded devices, or other kinds of workstations where compute may be
  -limited.
  +on laptops, embedded devices, or other kinds of workstations where the available
  +computing hardware is not well supported or the available software stack contains
  +unpalatable license restrictions.
  +
  +PlaidML sits underneath common machine learning frameworks, enabling users to
  +access any hardware supported by PlaidML. PlaidML supports Keras, ONNX, and nGraph.
  As a component within the [nGraph Compiler stack], PlaidML further extends the
  capabilities of specialized deep-learning hardware (especially GPUs,) and makes
  it both easier and faster to access or make use of subgraph-level optimizations
  -that would otherwise be bounded by the compute limitations of the device. The
  -latest version of PlaidML includes initial support for **PlaidML**'s ``Stripe``,
  -written in Tile. Tile was specifically created to be able to overcome the many
  -limitations of fussy, brittle and tightly-coupled 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 everywhere.
  +that would otherwise be bounded by the compute limitations of the device.
  +
  +The latest version of PlaidML includes initial support for **PlaidML**'s
  +``Stripe``. Tile was specifically created to be able to overcome the many
  +limitations of fussy, brittle and tightly-coupled 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 everywhere.
  As a component under [Keras], PlaidML can accelerate training workloads with
  customized or automatically-generated Tile code. It works especially well on
  