Update README.md

Author: nvcforster

TensorFlow/LanguageModeling/BERT/README.md

@@ -136,7 +136,7 @@ Multi-GPU training with Horovod - Our model uses Horovod to implement efficient
 
 [LAMB](https://arxiv.org/pdf/1904.00962.pdf) stands for Layerwise Adaptive Moments based optimizer, is a large batch optimization technique that helps accelerate training of deep neural networks using large minibatches. It allows using a global batch size of 65536 and 32768 on sequence lengths 128 and 512 respectively, compared to a batch size of 256 for Adam. The optimized implementation accumulates 1024 gradients batches in phase 1 and 4096 steps in phase 2 before updating weights once. This results in 27% training speedup on a single DGX2 node. On multi-node systems, LAMB allows scaling up to 1024 GPUs resulting in training speedups of up to 17x in comparison to [Adam](https://arxiv.org/pdf/1412.6980.pdf). Adam has limitations on the learning rate that can be used since it is applied globally on all parameters whereas LAMB follows a layerwise learning rate strategy.
 
-NVLAMB adds necessary tweaks to [LAMB version 1](https://arxiv.org/abs/1904.00962v1), to ensure correct convergence. The algorithm is as follows:
+NVLAMB adds necessary tweaks to [LAMB version 1](https://arxiv.org/abs/1904.00962v1), to ensure correct convergence. A guide to implementating the LAMB optimizer can be found in our [article](https://medium.com/@NvidiaAI/a-guide-to-optimizer-implementation-for-bert-at-scale-8338cc7f45fd) on Medium.com. The algorithm is as follows:
   ![NVLAMB](data/images/images_nvlamb.png)
 
 ### Mixed precision training
@@ -1152,4 +1152,4 @@ March 2019
 ### Known issues
 
 
-- There is a known performance regression with the 19.08 release on Tesla V100 boards with 16 GB memory, smaller batch sizes may be a better choice for this model on these GPUs with the 19.08 release. 32 GB GPUs are not affected.
+- There is a known performance regression with the 19.08 release on Tesla V100 boards with 16 GB memory, smaller batch sizes may be a better choice for this model on these GPUs with the 19.08 release. 32 GB GPUs are not affected.