loss backward spotlight

Author: vyinkabanjo

lab2/solutions/PT_Part1_MNIST_Solution.ipynb

@@ -658,7 +658,7 @@
       "source": [
         "### Train and test the CNN model\n",
         "\n",
-        "Earlier in the lab, we defined a `train` function. The body of the function is quite useful because it allows us to have control over the training model, and to record differentiation operations during training by computing the gradients using loss.backward(). You may recall seeing this in Lab 1 Part 1.\n",
+        "Earlier in the lab, we defined a `train` function. The body of the function is quite useful because it allows us to have control over the training model, and to record differentiation operations during training by computing the gradients using `loss.backward()`. You may recall seeing this in Lab 1 Part 1.\n",
         "\n",
         "We'll use this same framework to train our `cnn_model` using stochastic gradient descent. You are free to implement the following parts with or without the train and evaluate functions we defined above. What is most important is understanding how to manipulate the bodies of those functions to train and test models. \n",
         "\n",