Merge pull request tensorflow#18 from codyscott:patch-1

PiperOrigin-RevId: 238336483

courses/udacity_intro_to_tensorflow_for_deep_learning/l02c01_celsius_to_fahrenheit.ipynb

@@ -178,7 +178,7 @@
         "\n",
         "*   `input_shape=[1]` — This specifies that the input to this layer is a single value. That is, the shape is a one-dimensional array with one member. Since this is the first (and only) layer, that input shape is the input shape of the entire model. The single value is a floating point number, representing degrees Celsius.\n",
         "\n",
-        "*   `units=1` — This specifies the number of neurons in the layer. The number of neurons defines how many internal variables the layer has to try to learn how to solve the problem (more later). Since this is the final layer, it is also the size of the model's output — a single float value representing degrees Fahrenheit. (In a multi-layered network, the size and shape of the later would need to match the `input_shape` of the next layer.)\n"
+        "*   `units=1` — This specifies the number of neurons in the layer. The number of neurons defines how many internal variables the layer has to try to learn how to solve the problem (more later). Since this is the final layer, it is also the size of the model's output — a single float value representing degrees Fahrenheit. (In a multi-layered network, the size and shape of the layer would need to match the `input_shape` of the next layer.)\n"
       ]
     },
     {
@@ -284,7 +284,7 @@
         "\n",
         "The loss function ([mean squared error](https://en.wikipedia.org/wiki/Mean_squared_error)) and the optimizer ([Adam](https://machinelearningmastery.com/adam-optimization-algorithm-for-deep-learning/)) used here are standard for simple models like this one, but many others are available. It is not important to know how these specific functions work at this point.\n",
         "\n",
-        "One part of the Optimizer you may need to think about when building your own models is the learnign rate (`0.1` in the code above). This is the step size taken when adjusting values in the model. If the value is too small, it will take too many iterations to train the model. Too large, and accuracy goes down. Finding a good value often involves some trial and error, but the range is usually within 0.001 (default), and 0.1"
+        "One part of the Optimizer you may need to think about when building your own models is the learning rate (`0.1` in the code above). This is the step size taken when adjusting values in the model. If the value is too small, it will take too many iterations to train the model. Too large, and accuracy goes down. Finding a good value often involves some trial and error, but the range is usually within 0.001 (default), and 0.1"
       ]
     },
     {