Fix misspellings

04_mnist_basics.ipynb

@@ -3943,7 +3943,7 @@
     "\n",
     "Let's write such a function now. What form does it take?\n",
     "\n",
-    "The loss function receives not the images themseles, but the predictions from the model. Let's make one argument, `prds`, of values between 0 and 1, where each value is the prediction that an image is a 3. It is a vector (i.e., a rank-1 tensor), indexed over the images.\n",
+    "The loss function receives not the images themselves, but the predictions from the model. Let's make one argument, `prds`, of values between 0 and 1, where each value is the prediction that an image is a 3. It is a vector (i.e., a rank-1 tensor), indexed over the images.\n",
     "\n",
     "The purpose of the loss function is to measure the difference between predicted values and the true values — that is, the targets (aka labels). Let's make another argument, `trgts`, with values of 0 or 1 which tells whether an image actually is a 3 or not. It is also a vector (i.e., another rank-1 tensor), indexed over the images.\n",
     "\n",
@@ -4767,7 +4767,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Because this is such a general foundation, PyTorch provides some useful classes to make it easier to implement. The first thing we can do is replace our `linear` function with PyTorch's `nn.Linear` module. A *module* is an object of a class that inherits from the PyTorch `nn.Module` class. Objects of this class behave identically to standard Python functions, in that you can call them using parentheses and they will return the activations of a model.\n",
+    "Because this is such a general foundation, PyTorch provides some useful classes to make it easier to implement. The first thing we can do is replace our `linear1` function with PyTorch's `nn.Linear` module. A *module* is an object of a class that inherits from the PyTorch `nn.Module` class. Objects of this class behave identically to standard Python functions, in that you can call them using parentheses and they will return the activations of a model.\n",
     "\n",
     "`nn.Linear` does the same thing as our `init_params` and `linear` together. It contains both the *weights* and *biases* in a single class. Here's how we replicate our model from the previous section:"
    ]
@@ -5885,4 +5885,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}