From a32c5efcf58ce9d92047ef910f3a26a6ec268847 Mon Sep 17 00:00:00 2001
From: Baden Hughes <580499+badenh@users.noreply.github.com>
Date: Mon, 17 Feb 2020 11:56:25 +1000
Subject: [PATCH] Update faq.md (#4893)

various minor editorial updates - style, grammar, typos.
---
 docs/faq.md | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/docs/faq.md b/docs/faq.md
index f070ed59a575b..b5bf65eb52b0d 100644
--- a/docs/faq.md
+++ b/docs/faq.md
@@ -26,24 +26,24 @@ See [Installation](http://docs.tvm.ai/install/)
 TVM's relation to Other IR/DSL Projects
 ---------------------------------------
 There are usually two levels of abstractions of IR in the deep learning systems.
-TensorFlow's XLA and Intel's ngraph uses computation graph representation.
+TensorFlow's XLA and Intel's ngraph both use a computation graph representation.
 This representation is high level, and can be helpful to perform generic optimizations
 such as memory reuse, layout transformation and automatic differentiation.
 
-TVM adopts a low level representation, that explicitly express the choice of memory
+TVM adopts a low-level representation, that explicitly express the choice of memory
 layout, parallelization pattern, locality and hardware primitives etc.
 This level of IR is closer to directly target hardwares.
-The low level IR adopt ideas from existing image processing languages like Halide, darkroom
-and loop transformation tools like loopy and polyhedra based analysis.
-We specifically focus of expressing deep learning workloads(e.g. recurrence),
+The low-level IR adopts ideas from existing image processing languages like Halide, darkroom
+and loop transformation tools like loopy and polyhedra-based analysis.
+We specifically focus on expressing deep learning workloads (e.g. recurrence),
 optimization for different hardware backends and embedding with frameworks to provide
 end-to-end compilation stack.
 
 
-TVM's relation to libDNN cuDNN
+TVM's relation to libDNN, cuDNN
 ------------------------------
-TVM can incorporate these library as external calls. One goal of TVM is to be able to
-generate high performing kernels. We will evolve TVM an incremental manner as
-we learn from the technics of manual kernel crafting and add these as primitives in DSL.
+TVM can incorporate these libraries as external calls. One goal of TVM is to be able to
+generate high-performing kernels. We will evolve TVM an incremental manner as
+we learn from the techniques of manual kernel crafting and add these as primitives in DSL.
 See also [TVM Operator Inventory](https://github.com/apache/incubator-tvm/tree/master/topi) for
 recipes of operators in TVM.