Merge pull request #1074 from pritesh2000/gram-1/00

00_pytorch_fundamentals.ipynb

00_pytorch_fundamentals.ipynb

@@ -30,11 +30,11 @@
     "\n",
     "## Who uses PyTorch?\n",
     "\n",
-    "Many of the worlds largest technology companies such as [Meta (Facebook)](https://ai.facebook.com/blog/pytorch-builds-the-future-of-ai-and-machine-learning-at-facebook/), Tesla and Microsoft as well as artificial intelligence research companies such as [OpenAI use PyTorch](https://openai.com/blog/openai-pytorch/) to power research and bring machine learning to their products.\n",
+    "Many of the world's largest technology companies such as [Meta (Facebook)](https://ai.facebook.com/blog/pytorch-builds-the-future-of-ai-and-machine-learning-at-facebook/), Tesla and Microsoft as well as artificial intelligence research companies such as [OpenAI use PyTorch](https://openai.com/blog/openai-pytorch/) to power research and bring machine learning to their products.\n",
     "\n",
     "![pytorch being used across industry and research](https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/00-pytorch-being-used-across-research-and-industry.png)\n",
     "\n",
-    "For example, Andrej Karpathy (head of AI at Tesla) has given several talks ([PyTorch DevCon 2019](https://youtu.be/oBklltKXtDE), [Tesla AI Day 2021](https://youtu.be/j0z4FweCy4M?t=2904)) about how Tesla use PyTorch to power their self-driving computer vision models.\n",
+    "For example, Andrej Karpathy (head of AI at Tesla) has given several talks ([PyTorch DevCon 2019](https://youtu.be/oBklltKXtDE), [Tesla AI Day 2021](https://youtu.be/j0z4FweCy4M?t=2904)) about how Tesla uses PyTorch to power their self-driving computer vision models.\n",
     "\n",
     "PyTorch is also used in other industries such as agriculture to [power computer vision on tractors](https://medium.com/pytorch/ai-for-ag-production-machine-learning-for-agriculture-e8cfdb9849a1).\n",
     "\n",
@@ -66,7 +66,7 @@
     "| **Creating tensors** | Tensors can represent almost any kind of data (images, words, tables of numbers). |\n",
     "| **Getting information from tensors** | If you can put information into a tensor, you'll want to get it out too. |\n",
     "| **Manipulating tensors** | Machine learning algorithms (like neural networks) involve manipulating tensors in many different ways such as adding, multiplying, combining. | \n",
-    "| **Dealing with tensor shapes** | One of the most common issues in machine learning is dealing with shape mismatches (trying to mixed wrong shaped tensors with other tensors). |\n",
+    "| **Dealing with tensor shapes** | One of the most common issues in machine learning is dealing with shape mismatches (trying to mix wrong shaped tensors with other tensors). |\n",
     "| **Indexing on tensors** | If you've indexed on a Python list or NumPy array, it's very similar with tensors, except they can have far more dimensions. |\n",
     "| **Mixing PyTorch tensors and NumPy** | PyTorch plays with tensors ([`torch.Tensor`](https://pytorch.org/docs/stable/tensors.html)), NumPy likes arrays ([`np.ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html)) sometimes you'll want to mix and match these. | \n",
     "| **Reproducibility** | Machine learning is very experimental and since it uses a lot of *randomness* to work, sometimes you'll want that *randomness* to not be so random. |\n",
@@ -501,7 +501,7 @@
     "id": "LhXXgq-dTGe3"
    },
    "source": [
-    "`MATRIX` has two dimensions (did you count the number of square brakcets on the outside of one side?).\n",
+    "`MATRIX` has two dimensions (did you count the number of square brackets on the outside of one side?).\n",
     "\n",
     "What `shape` do you think it will have?"
    ]
@@ -697,7 +697,7 @@
     "\n",
     "And machine learning models such as neural networks manipulate and seek patterns within tensors.\n",
     "\n",
-    "But when building machine learning models with PyTorch, it's rare you'll create tensors by hand (like what we've being doing).\n",
+    "But when building machine learning models with PyTorch, it's rare you'll create tensors by hand (like what we've been doing).\n",
     "\n",
     "Instead, a machine learning model often starts out with large random tensors of numbers and adjusts these random numbers as it works through data to better represent it.\n",
     "\n",
@@ -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)."
    ]
   },