Update tf.Tensors in Customization basics tutorial

Author: 8bitmp3

site/en/tutorials/customization/basics.ipynb

@@ -106,7 +106,7 @@
       "source": [
         "## Tensors\n",
         "\n",
-        "A Tensor is a multi-dimensional array. Similar to NumPy `ndarray` objects, `tf.Tensor` objects have a data type and a shape. Additionally, `tf.Tensor` can reside in accelerator memory (like a GPU). TensorFlow offers a rich library of operations (for example, `tf.math.add`, `tf.linalg.matmul`, and `tf.linalg.inv`) that consume and produce `tf.Tensor`. These operations automatically convert built-in Python types. For example:\n"
+        "A Tensor is a multi-dimensional array. Similar to NumPy `ndarray` objects, `tf.Tensor` objects have a data type and a shape. Additionally, `tf.Tensor`s can reside in accelerator memory (like a GPU). TensorFlow offers a rich library of operations (for example, `tf.math.add`, `tf.linalg.matmul`, and `tf.linalg.inv`) that consume and produce `tf.Tensor`s. These operations automatically convert built-in Python types. For example:\n"
       ]
     },
     {
@@ -156,7 +156,7 @@
         "id": "eBPw8e8vrsom"
       },
       "source": [
-        "The most obvious differences between NumPy arrays and `tf.Tensor` are:\n",
+        "The most obvious differences between NumPy arrays and `tf.Tensor`s are:\n",
         "\n",
         "1. Tensors can be backed by accelerator memory (like GPU, TPU).\n",
         "2. Tensors are immutable."