Fix typos in tutorials 1 and 2 (#355)

Signed-off-by: LateNightIceCream <[email protected]>

examples/Sionna_tutorial_part1.ipynb

@@ -411,7 +411,7 @@
    "source": [
     "As can be seen, the `Mapper` class inherits from `Layer`, i.e., implements a Keras layer.\n",
     "\n",
-    "This allows to simply built complex systems by using the [Keras functional API](https://keras.io/guides/functional_api/) to stack layers."
+    "This allows to simply build complex systems by using the [Keras functional API](https://keras.io/guides/functional_api/) to stack layers."
    ]
   },
   {
@@ -521,7 +521,7 @@
    "id": "aca7a98b",
    "metadata": {},
    "source": [
-    "In *Eager* mode, we can directly access the values of each tensor. This simplify debugging."
+    "In *Eager* mode, we can directly access the values of each tensor. This simplifies debugging."
    ]
   },
   {
@@ -613,7 +613,7 @@
    "id": "bfc184ba-c090-4443-9cd6-c217b3f64052",
    "metadata": {},
    "source": [
-    "It is typically more convenient to wrap a Sionna-based communication system into a [Keras models](https://keras.io/api/models/model/).\n",
+    "It is typically more convenient to wrap a Sionna-based communication system into a [Keras model](https://keras.io/api/models/model/).\n",
     "\n",
     "These models can be simply built by using the [Keras functional API](https://keras.io/guides/functional_api/) to stack layers.\n",
     "\n",
@@ -854,10 +854,10 @@
    "metadata": {},
    "source": [
     "One of the fundamental paradigms of Sionna is batch-processing.\n",
-    "Thus, the example above could be executed with for arbitrary batch-sizes to simulate `batch_size` codewords in parallel.\n",
+    "Thus, the example above could be executed for arbitrary batch-sizes to simulate `batch_size` codewords in parallel.\n",
     "\n",
     "However, Sionna can do more - it supports *N*-dimensional input tensors and, thereby, allows the processing of multiple samples of multiple users and several antennas in a single command line.\n",
-    "Let's say we want to encoded `batch_size` codewords of length `n` for each of the `num_users` connected to each of the `num_basestations`. \n",
+    "Let's say we want to encode `batch_size` codewords of length `n` for each of the `num_users` connected to each of the `num_basestations`. \n",
     "This means in total we transmit `batch_size` * `n` * `num_users` * `num_basestations` bits."
    ]
   },

examples/Sionna_tutorial_part2.ipynb

@@ -290,7 +290,7 @@
    "id": "318aa681",
    "metadata": {},
    "source": [
-    "`gradient` is a list of tensor, each tensor corresponding to a trainable variable of our model.\n",
+    "`gradient` is a list of tensors, each tensor corresponding to a trainable variable of our model.\n",
     "\n",
     "For this model, we only have a single trainable tensor: The constellation of shape [`2`, `2^NUM_BITS_PER_SYMBOL`], the first dimension corresponding to the real and imaginary components of the constellation points.\n",
     "\n",
@@ -357,7 +357,7 @@
    "id": "d9ef8110",
    "metadata": {},
    "source": [
-    "Let compare the constellation before and after the gradient application"
+    "Let's compare the constellation before and after the gradient application"
    ]
   },
   {