diff --git a/docs/tutorials/layers_normalizations.ipynb b/docs/tutorials/layers_normalizations.ipynb
index 01fffc7aa7..0788784667 100644
--- a/docs/tutorials/layers_normalizations.ipynb
+++ b/docs/tutorials/layers_normalizations.ipynb
@@ -67,7 +67,7 @@
         "* **Instance Normalization** (TensorFlow Addons)\n",
         "* **Layer Normalization** (TensorFlow Core)\n",
         "\n",
-        "The basic idea behind these layers is to normalize the output of an activation layer to improve the convergence during training. In contrast to [batch normalization](https://keras.io/layers/normalization/) these normalizations do not work on batches, instead they normalize the activations of a single sample, making them suitable for recurrent neual networks as well. \n",
+        "The basic idea behind these layers is to normalize the output of an activation layer to improve the convergence during training. In contrast to [batch normalization](https://keras.io/layers/normalization/) these normalizations do not work on batches, instead they normalize the activations of a single sample, making them suitable for recurrent neural networks as well. \n",
         "\n",
         "Typically the normalization is performed by calculating the mean and the standard deviation of a subgroup in your input tensor. It is also possible to apply a scale and an offset factor to this as well.\n",
         "\n",
@@ -260,7 +260,7 @@
         "### Introduction\n",
         "Layer Normalization is special case of group normalization where the group size is 1. The mean and standard deviation is calculated from all activations of a single sample.\n",
         "\n",
-        "Experimental results show that Layer normalization is well suited for Recurrent Neural Networks, since it works batchsize independt.\n",
+        "Experimental results show that Layer normalization is well suited for Recurrent Neural Networks, since it works batchsize independently.\n",
         "\n",
         "### Example\n",
         "\n",