diff --git a/00_pytorch_fundamentals.ipynb b/00_pytorch_fundamentals.ipynb
index 04b06806..892cd772 100644
--- a/00_pytorch_fundamentals.ipynb
+++ b/00_pytorch_fundamentals.ipynb
@@ -984,7 +984,7 @@
     "\n",
     "Some are specific for CPU and some are better for GPU.\n",
     "\n",
-    "Getting to know which is which can take some time.\n",
+    "Getting to know which one can take some time.\n",
     "\n",
     "Generally if you see `torch.cuda` anywhere, the tensor is being used for GPU (since Nvidia GPUs use a computing toolkit called CUDA).\n",
     "\n",
@@ -1901,7 +1901,7 @@
     "id": "bXKozI4T0hFi"
    },
    "source": [
-    "Without the transpose, the rules of matrix mulitplication aren't fulfilled and we get an error like above.\n",
+    "Without the transpose, the rules of matrix multiplication aren't fulfilled and we get an error like above.\n",
     "\n",
     "How about a visual? \n",
     "\n",
@@ -1988,7 +1988,7 @@
     "id": "zIGrP5j1pN7j"
    },
    "source": [
-    "> **Question:** What happens if you change `in_features` from 2 to 3 above? Does it error? How could you change the shape of the input (`x`) to accomodate to the error? Hint: what did we have to do to `tensor_B` above?"
+    "> **Question:** What happens if you change `in_features` from 2 to 3 above? Does it error? How could you change the shape of the input (`x`) to accommodate to the error? Hint: what did we have to do to `tensor_B` above?"
    ]
   },
   {
@@ -2188,7 +2188,7 @@
     "\n",
     "You can change the datatypes of tensors using [`torch.Tensor.type(dtype=None)`](https://pytorch.org/docs/stable/generated/torch.Tensor.type.html) where the `dtype` parameter is the datatype you'd like to use.\n",
     "\n",
-    "First we'll create a tensor and check it's datatype (the default is `torch.float32`)."
+    "First we'll create a tensor and check its datatype (the default is `torch.float32`)."
    ]
   },
   {
@@ -2289,7 +2289,7 @@
     }
    ],
    "source": [
-    "# Create a int8 tensor\n",
+    "# Create an int8 tensor\n",
     "tensor_int8 = tensor.type(torch.int8)\n",
     "tensor_int8"
    ]
@@ -3139,7 +3139,7 @@
    "source": [
     "Just as you might've expected, the tensors come out with different values.\n",
     "\n",
-    "But what if you wanted to created two random tensors with the *same* values.\n",
+    "But what if you wanted to create two random tensors with the *same* values.\n",
     "\n",
     "As in, the tensors would still contain random values but they would be of the same flavour.\n",
     "\n",
@@ -3220,7 +3220,7 @@
     "It looks like setting the seed worked. \n",
     "\n",
     "> **Resource:** What we've just covered only scratches the surface of reproducibility in PyTorch. For more, on reproducibility in general and random seeds, I'd checkout:\n",
-    "> * [The PyTorch reproducibility documentation](https://pytorch.org/docs/stable/notes/randomness.html) (a good exericse would be to read through this for 10-minutes and even if you don't understand it now, being aware of it is important).\n",
+    "> * [The PyTorch reproducibility documentation](https://pytorch.org/docs/stable/notes/randomness.html) (a good exercise would be to read through this for 10-minutes and even if you don't understand it now, being aware of it is important).\n",
     "> * [The Wikipedia random seed page](https://en.wikipedia.org/wiki/Random_seed) (this'll give a good overview of random seeds and pseudorandomness in general)."
    ]
   },