Update content/tutorial-nlp-from-scratch.md

Co-authored-by: Mukulika <60316606+Mukulikaa@users.noreply.github.com>

Author: star1327p

content/tutorial-nlp-from-scratch.md

@@ -1049,7 +1049,7 @@ To further enhance and optimize your neural network model, you can consider one
 - Initialize weights using [Xavier Initialization](https://d2l.ai/chapter_multilayer-perceptrons/numerical-stability-and-init.html#xavier-initialization) to prevent vanishing/exploding gradients instead of initializing them randomly.
 - Replace LSTM with a [Bidirectional LSTM](https://en.wikipedia.org/wiki/Bidirectional_recurrent_neural_networks) to use both left and right context for predicting sentiment.
 
-Nowadays, LSTMs have been replaced by the [Transformer](https://jalammar.github.io/illustrated-transformer/) (which uses [Attention](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/) to tackle all the problems that plague an LSTM such as as lack of [transfer learning](https://en.wikipedia.org/wiki/Transfer_learning), lack of [parallel training](https://web.stanford.edu/~rezab/classes/cme323/S16/projects_reports/hedge_usmani.pdf) and a long gradient chain for lengthy sequences).
+Nowadays, LSTMs have been replaced by the [Transformer](https://jalammar.github.io/illustrated-transformer/) which uses [Attention](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/) to tackle all the problems that plague an LSTM such as lack of [transfer learning](https://en.wikipedia.org/wiki/Transfer_learning), lack of [parallel training](https://web.stanford.edu/~rezab/classes/cme323/S16/projects_reports/hedge_usmani.pdf), and a long gradient chain for lengthy sequences.
 
 Building a neural network from scratch with NumPy is a great way to learn more about NumPy and about deep learning. However, for real-world applications you should use specialized frameworks — such as PyTorch, JAX or TensorFlow — that provide NumPy-like APIs, have built-in automatic differentiation and GPU support, and are designed for high-performance numerical computing and machine learning.