fix -typos

Author: pritesh2000

extras/pytorch_2_intro.ipynb

@@ -175,7 +175,7 @@
     "\n",
     "Why?\n",
     "\n",
-    "Modern GPUs have so much compute power they are often not compute limited, as in, the main bottleneck to training models is how fast can you get data from your CPU to your GPU.\n",
+    "Modern GPUs have so much compute power they are often not compute limited, as in, the main bottleneck to training models is how fast you can get data from your CPU to your GPU.\n",
     "This is known as bandwidth or memory bandwidth.\n",
     "\n",
     "You want to reduce your bandwidth costs as much as possible.\n",
@@ -209,7 +209,7 @@
     "\n",
     "Graph capture I’m less confident explaining.\n",
     "\n",
-    "But the way I think about is that graph capture or graph tracing is:\n",
+    "But the way I think about it is that graph capture or graph tracing is:\n",
     "\n",
     "* Going through a series of operations that need to happen, such as the operations in a neural network.\n",
     "* And capturing or tracing what needs to happen ahead of time.\n",
@@ -272,7 +272,7 @@
    "source": [
     "## What we're going to cover\n",
     "\n",
-    "Since many of the upgrades in PyTorch 2.0 are speed focused and happen behind the scenes (e.g. PyTorch takes care of them for you), in this notebook we're going to run a compartive speed test.\n",
+    "Since many of the upgrades in PyTorch 2.0 are speed focused and happen behind the scenes (e.g. PyTorch takes care of them for you), in this notebook we're going to run a comparative speed test.\n",
     "\n",
     "Namely we'll make two of the same models, one using the default PyTorch setup and the other using the new `torch.compile()` setup and we'll train them on the same dataset.\n",
     "\n",
@@ -391,7 +391,7 @@
     "\n",
     "And GPUs which are datacenter-class (e.g. A100, A10, H100) are likely to see more significant speedups than desktop-class GPUs (e.g. RTX 3090, RTX 3080, RTX 3070, RTX 3060 Ti).\n",
     "\n",
-    "We can check the compute capbility score of our GPU using [`torch.cuda.get_device_capability()`](https://pytorch.org/docs/stable/generated/torch.cuda.get_device_capability.html).\n",
+    "We can check the compute capability score of our GPU using [`torch.cuda.get_device_capability()`](https://pytorch.org/docs/stable/generated/torch.cuda.get_device_capability.html).\n",
     "\n",
     "This will output a tuple of `(major, minor)` compute capability scores, for example, `(8, 0)` for the A100.\n",
     "\n",
@@ -737,7 +737,7 @@
     "* **Increasing the batch size** - More samples per batch means more samples on the GPU, for example, using a batch size of 256 instead of 32.\n",
     "* **Increasing data size** - For example, using larger image size, 224x224 instead of 32x32. A larger data size means that more tensor operations will be happening on the GPU.\n",
     "* **Increasing model size** - For example, using a larger model such as ResNet101 instead of ResNet50. A larger model means that more tensor operations will be happening on the GPU.\n",
-    "* **Decreasing data transfer** - For example, setting up all your tensors to be on GPU memory, this minizes the amount of data transfer between the CPU and GPU.\n",
+    "* **Decreasing data transfer** - For example, setting up all your tensors to be on GPU memory, this minimizes the amount of data transfer between the CPU and GPU.\n",
     "\n",
     "All of these result in *more* data being on the GPU.\n",
     "\n",