Update

clean/10_nlp.ipynb

@@ -1489,6 +1489,13 @@
     "## Disinformation and language models"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

clean/11_midlevel_data.ipynb

@@ -797,6 +797,56 @@
     "    after_batch=[IntToFloatTensor, Normalize.from_stats(*imagenet_stats)])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Questionnaire"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Why do we say that fastai has a layered API? What does it mean?\n",
+    "1. Why does a `Transform` have a decode method? What does it do?\n",
+    "1. Why does a `Transform` have a setup method? What does it do?\n",
+    "1. How does a `Transform` work when called on a tuple?\n",
+    "1. Which methods do you need to implement when writing your own `Transform`?\n",
+    "1. Write a `Normalize` transform that fully normalizes items (substract the mean and divide by the standard deviation of the dataset), and that can decode that behavior. Try not to peak!\n",
+    "1. Write a `Transform` that does the numericalization of tokenized texts (it should set its vocab automatically from the dataset seen and have a decode method). Look at the source code of fastai if you need help.\n",
+    "1. What is a `Pipeline`?\n",
+    "1. What is a `TfmdLists`? \n",
+    "1. What is a `Datasets`? How is it different from `TfmdLists`?\n",
+    "1. Why are `TfmdLists` and `Datasets` named with an s?\n",
+    "1. How can you build a `DataLoaders` from a `TfmdLists` or a `Datasets`?\n",
+    "1. How do you pass `item_tfms` and `batch_tfms` when building a `DataLoaders` from a `TfmdLists` or a `Datasets`?\n",
+    "1. What do you need to do when you want to have your custom items work with methods like `show_batch` or `show_results`?\n",
+    "1. Why can we easily apply fastai data augmentation transforms to the `SiamesePair` we built?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Further research"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Use the mid-level API to grab the data on the pets dataset. On the adult dataset (used in chapter 1).\n",
+    "1. Look at the siamese tutorial in the fastai documentation to learn how to customize the behavior of `show_batch` and `show_results` for new type of items. Implement it on your own project."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -804,6 +854,17 @@
     "## Becoming a deep learning practitioner"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Congratulations — you've completed all of the chapters in this book which cover the key practical parts of training and using deep learning! You know how to use all of fastai's built in applications, and how to customise them using the data blocks API and loss functions. You even know how to create a neural network from scratch, and train it! (And hopefully you now know some of the questions to ask to help make sure your creations help improve society too.)\n",
+    "\n",
+    "The knowledge you already have is enough to create full working prototypes of many types of neural network application. More importantly, it will help you understand the capabilities and limitations of deep learning models, and how to design a system which best handles these capabilities and limitations.\n",
+    "\n",
+    "In the rest of this book we will be pulling apart these applications, piece by piece, to understand all of the foundations they are built on. This is important knowledge for a deep learning practitioner, because it is the knowledge which allows you to inspect and debug models that you build, and to create new applications which are customised for your particular projects."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

clean/14_resnet.ipynb

@@ -811,6 +811,13 @@
     "learn.fit_one_cycle(20, 3e-3)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

clean/16_accel_sgd.ipynb

@@ -663,6 +663,13 @@
     "            raise CancelFitException"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

clean/17_foundations.ipynb

@@ -1513,7 +1513,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Things to remember"
+    "## Conclusion"
    ]
   },
   {

clean/18_CAM.ipynb

@@ -413,6 +413,13 @@
     "              interpolation='bilinear', cmap='magma');"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

clean/19_learner.ipynb

@@ -1270,6 +1270,13 @@
     "plt.plot(onecyc.lrs);"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},