merge

01_intro.ipynb

@@ -947,7 +947,7 @@
     "\n",
     "Let us take these concepts one by one, in order to understand how they fit together in practice. First, we need to understand what Samuel means by a *weight assignment*.\n",
     "\n",
-    "Weights are just variables, and a weight assignment is a particular choice of values for those variables. The program's inputs are values that it processes in order to products its results -- for instance,  taking image pixels as inputs, and returning the classification \"dog\" as a result. But the program's weight assignments are other values which define how the program will operate.\n",
+    "Weights are just variables, and a weight assignment is a particular choice of values for those variables. The program's inputs are values that it processes in order to produce its results -- for instance,  taking image pixels as inputs, and returning the classification \"dog\" as a result. But the program's weight assignments are other values which define how the program will operate.\n",
     "\n",
     "Since they will affect the program they are in a sense another kind of input, so we will update our basic picture of <<basic_program>> and replace it with <<weight_assignment>> in order to take this into account:"
    ]

07_sizing_and_tta.ipynb

@@ -835,7 +835,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This all done for us inside fastai by adding a `Callback` to our `Learner`. `Callback`s are what is used inside fastai to inject custom behavior in the training loop (like a learning rate schedule, or training in mixed precision). We'll be learning all about callbacks, including how to make your own, in <<chapter_callbacks>>. For now, all you need to know is that you use the `cbs` parameter to `Learner` to pass callbacks.\n",
+    "This all done for us inside fastai by adding a `Callback` to our `Learner`. `Callback`s are what is used inside fastai to inject custom behavior in the training loop (like a learning rate schedule, or training in mixed precision). We'll be learning all about callbacks, including how to make your own, in <<chapter_accel_sgd>>. For now, all you need to know is that you use the `cbs` parameter to `Learner` to pass callbacks.\n",
     "\n",
     "Here is how you train a model with Mixup:\n",
     "\n",