message: Adjust some expression in User Guide
diff: @@ -11,13 +11,13 @@ Highlighted features of HugeCTR
  ## New Features in Version 2.1
  HugeCTR version 2.1 is a major update which aims to provide a flexible, fast, scalable and reliable solution for CTR Training. Framework designers can consider it as a high-performance reference design.
  -* Supporting three important networks: Wide and Deep Learning (WDL)[1], Deep Cross Network (DCN)[2] and DeepFM [3]
  -* A new embedding implementation `LocalizedSlotSparseEmbedding` which reduces the memory transactions across GPUs and nodes resiliently to the number of GPUs.
  -* Supporting multiple Embeddings in one network
  -* Supporting dense feature input, which doesn't need any embdding layer
  -* Supporting new layers like: Dropout / Split / Reshape / Multiply / FmOrder2 / MultCross / Add
  -* Check bits in data reader to enable data check and error skip.
  -* L1 / L2 Regularization
  +* Supporting three important networks: Wide and Deep Learning (WDL)[1], Deep Cross Network (DCN)[2] and DeepFM [3];
  +* A new embedding implementation `LocalizedSlotSparseEmbedding` which reduces the memory transactions across GPUs and nodes resiliently to the number of GPUs;
  +* Supporting multiple Embeddings in one network;
  +* Supporting dense feature input;
  +* Supporting new layers like: Dropout / Split / Reshape / Multiply / FmOrder2 / MultCross / Add;
  +* Check bits in data reader to enable data check and error skip;
  +* L1 / L2 Regularization.
  ## Architecture and Supported Networks
  To enable large embedding training, the embedding table in HugeCTR is model parallel and distributed across all the GPUs in a homogeneous cluster, which consists of multiple nodes and multiple GPUs. Meanwhile, the dense model such as DNN is data parallel, which has one copy in each GPU (see Fig.1).
  