Update and add clean

02_production.ipynb

@@ -1629,7 +1629,8 @@
    ],
    "source": [
     "#hide_output\n",
-    "VBox([widgets.Label('Select your bear!'), btn_upload, btn_run, out_pl, lbl_pred])"
+    "VBox([widgets.Label('Select your bear!'), \n",
+    "      btn_upload, btn_run, out_pl, lbl_pred])"
    ]
   },
   {
@@ -1946,20 +1947,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

03_ethics.ipynb

@@ -1041,34 +1041,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {
-    "height": "600px",
-    "width": "365px"
-   },
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
   }
  },
  "nbformat": 4,

05_pet_breeds.ipynb

@@ -2543,20 +2543,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

06_multicat.ipynb

@@ -1445,11 +1445,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "biwi = DataBlock(blocks=(ImageBlock, PointBlock),\n",
-    "                 get_items=get_image_files,\n",
-    "                 get_y=get_ctr,\n",
-    "                 splitter=FuncSplitter(lambda o: o.parent.name=='13'),\n",
-    "                 batch_tfms=[*aug_transforms(size=(240,320)), Normalize.from_stats(*imagenet_stats)])"
+    "biwi = DataBlock(\n",
+    "    blocks=(ImageBlock, PointBlock),\n",
+    "    get_items=get_image_files,\n",
+    "    get_y=get_ctr,\n",
+    "    splitter=FuncSplitter(lambda o: o.parent.name=='13'),\n",
+    "    batch_tfms=[*aug_transforms(size=(240,320)), \n",
+    "                Normalize.from_stats(*imagenet_stats)]\n",
+    ")"
    ]
   },
   {
@@ -1925,6 +1928,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

07_sizing_and_tta.ipynb

@@ -460,7 +460,8 @@
    ],
    "source": [
     "dls = get_dls(128, 128)\n",
-    "learn = Learner(dls, xresnet50(), loss_func=CrossEntropyLossFlat(), metrics=accuracy)\n",
+    "learn = Learner(dls, xresnet50(), loss_func=CrossEntropyLossFlat(), \n",
+    "                metrics=accuracy)\n",
     "learn.fit_one_cycle(4, 3e-3)"
    ]
   },
@@ -1011,6 +1012,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

08_collab.ipynb

@@ -2296,6 +2296,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

09_tabular.ipynb

@@ -9740,31 +9740,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
   }
  },
  "nbformat": 4,

10_nlp.ipynb

@@ -1708,7 +1708,8 @@
     "TEXT = \"I liked this movie because\"\n",
     "N_WORDS = 40\n",
     "N_SENTENCES = 2\n",
-    "preds = [learn.predict(TEXT, N_WORDS, temperature=0.75) for _ in range(N_SENTENCES)]"
+    "preds = [learn.predict(TEXT, N_WORDS, temperature=0.75) \n",
+    "         for _ in range(N_SENTENCES)]"
    ]
   },
   {
@@ -1891,7 +1892,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "learn = text_classifier_learner(dls_clas, AWD_LSTM, drop_mult=0.5, metrics=accuracy).to_fp16()"
+    "learn = text_classifier_learner(dls_clas, AWD_LSTM, drop_mult=0.5, \n",
+    "                                metrics=accuracy).to_fp16()"
    ]
   },
   {
@@ -2260,20 +2262,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

11_midlevel_data.ipynb

@@ -602,7 +602,8 @@
    "source": [
     "cut = int(len(files)*0.8)\n",
     "splits = [list(range(cut)), list(range(cut,len(files)))]\n",
-    "tls = TfmdLists(files, [Tokenizer.from_folder(path), Numericalize], splits=splits)"
+    "tls = TfmdLists(files, [Tokenizer.from_folder(path), Numericalize], \n",
+    "                splits=splits)"
    ]
   },
   {
@@ -1083,7 +1084,8 @@
     "class SiameseTransform(Transform):\n",
     "    def __init__(self, files, label_func, splits):\n",
     "        self.labels = files.map(label_func).unique()\n",
-    "        self.lbl2files = {l: L(f for f in files if label_func(f) == l) for l in self.labels}\n",
+    "        self.lbl2files = {l: L(f for f in files if label_func(f) == l) \n",
+    "                          for l in self.labels}\n",
     "        self.label_func = label_func\n",
     "        self.valid = {f: self._draw(f) for f in files[splits[1]]}\n",
     "        \n",
@@ -1095,7 +1097,8 @@
     "    def _draw(self, f):\n",
     "        same = random.random() < 0.5\n",
     "        cls = self.label_func(f)\n",
-    "        if not same: cls = random.choice(L(l for l in self.labels if l != cls)) \n",
+    "        if not same: \n",
+    "            cls = random.choice(L(l for l in self.labels if l != cls)) \n",
     "        return random.choice(self.lbl2files[cls]),same"
    ]
   },
@@ -1246,20 +1249,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

12_nlp_dive.ipynb

@@ -497,7 +497,8 @@
     }
    ],
    "source": [
-    "learn = Learner(dls, LMModel1(len(vocab), 64), loss_func=F.cross_entropy, metrics=accuracy)\n",
+    "learn = Learner(dls, LMModel1(len(vocab), 64), loss_func=F.cross_entropy, \n",
+    "                metrics=accuracy)\n",
     "learn.fit_one_cycle(4, 1e-3)"
    ]
   },
@@ -654,7 +655,8 @@
     }
    ],
    "source": [
-    "learn = Learner(dls, LMModel2(len(vocab), 64), loss_func=F.cross_entropy, metrics=accuracy)\n",
+    "learn = Learner(dls, LMModel2(len(vocab), 64), loss_func=F.cross_entropy, \n",
+    "                metrics=accuracy)\n",
     "learn.fit_one_cycle(4, 1e-3)"
    ]
   },
@@ -1474,7 +1476,8 @@
     }
    ],
    "source": [
-    "learn = Learner(dls, LMModel5(len(vocab), 64, 2), loss_func=CrossEntropyLossFlat(), \n",
+    "learn = Learner(dls, LMModel5(len(vocab), 64, 2), \n",
+    "                loss_func=CrossEntropyLossFlat(), \n",
     "                metrics=accuracy, cbs=ModelReseter)\n",
     "learn.fit_one_cycle(15, 3e-3)"
    ]
@@ -1867,7 +1870,8 @@
     }
    ],
    "source": [
-    "learn = Learner(dls, LMModel6(len(vocab), 64, 2), loss_func=CrossEntropyLossFlat(), \n",
+    "learn = Learner(dls, LMModel6(len(vocab), 64, 2), \n",
+    "                loss_func=CrossEntropyLossFlat(), \n",
     "                metrics=accuracy, cbs=ModelReseter)\n",
     "learn.fit_one_cycle(15, 1e-2)"
    ]
@@ -2354,20 +2358,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

13_convolutions.ipynb

@@ -3757,6 +3757,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

14_resnet.ipynb

@@ -1268,6 +1268,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

15_arch_details.ipynb

@@ -233,7 +233,341 @@
    "source": [
     "This picture shows the CNN body on the left (in this case, it's a regular CNN, not a ResNet, and they're using 2x2 max pooling instead of stride 2 convolutions, since this paper was written before ResNets came along) and it shows the transposed convolutional layers on the right (they're called \"up-conv\" in this picture). Then then extra skip connections are shown as grey arrows crossing from left to right (these are sometimes called *cross connections*). You can see why it's called a \"U-net\" when you see this picture!\n",
     "\n",
-    "With this architecture, the input to the transposed convolutions is not just the lower resolution grid in the preceding layer, but also the higher resolution grid in the resnet head. This allows the U-Net to use all of the information of the original image, as it is needed. One challenge with U-Nets is that the exact architecture depends on the image size. fastai has a unique `DynamicUnet` class which auto-generates an architecture of the right size based on the data provided."
+    "With this architecture, the input to the transposed convolutions is not just the lower resolution grid in the preceding layer, but also the higher resolution grid in the resnet head. This allows the U-Net to use all of the information of the original image, as it is needed. One challenge with U-Nets is that the exact architecture depends on the image size. fastai has a unique `DynamicUnet` class which auto-generates an architecture of the right size based on the data provided.\n",
+    "\n",
+    "Let's focus now on an example where we leverage the fastai library to write a custom model:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A Siamese network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#hide\n",
+    "from fastai2.vision.all import *\n",
+    "path = untar_data(URLs.PETS)\n",
+    "files = get_image_files(path/\"images\")\n",
+    "\n",
+    "class SiameseImage(Tuple):\n",
+    "    def show(self, ctx=None, **kwargs): \n",
+    "        img1,img2,same_breed = self\n",
+    "        if not isinstance(img1, Tensor):\n",
+    "            if img2.size != img1.size: img2 = img2.resize(img1.size)\n",
+    "            t1,t2 = tensor(img1),tensor(img2)\n",
+    "            t1,t2 = t1.permute(2,0,1),t2.permute(2,0,1)\n",
+    "        else: t1,t2 = img1,img2\n",
+    "        line = t1.new_zeros(t1.shape[0], t1.shape[1], 10)\n",
+    "        return show_image(torch.cat([t1,line,t2], dim=2), \n",
+    "                          title=same_breed, ctx=ctx)\n",
+    "    \n",
+    "def label_func(fname):\n",
+    "    return re.match(r'^(.*)_\\d+.jpg$', fname.name).groups()[0]\n",
+    "\n",
+    "class SiameseTransform(Transform):\n",
+    "    def __init__(self, files, label_func, splits):\n",
+    "        self.labels = files.map(label_func).unique()\n",
+    "        self.lbl2files = {l: L(f for f in files if label_func(f) == l) for l in self.labels}\n",
+    "        self.label_func = label_func\n",
+    "        self.valid = {f: self._draw(f) for f in files[splits[1]]}\n",
+    "        \n",
+    "    def encodes(self, f):\n",
+    "        f2,t = self.valid.get(f, self._draw(f))\n",
+    "        img1,img2 = PILImage.create(f),PILImage.create(f2)\n",
+    "        return SiameseImage(img1, img2, t)\n",
+    "    \n",
+    "    def _draw(self, f):\n",
+    "        same = random.random() < 0.5\n",
+    "        cls = self.label_func(f)\n",
+    "        if not same: cls = random.choice(L(l for l in self.labels if l != cls)) \n",
+    "        return random.choice(self.lbl2files[cls]),same\n",
+    "    \n",
+    "splits = RandomSplitter()(files)\n",
+    "tfm = SiameseTransform(files, label_func, splits)\n",
+    "tls = TfmdLists(files, tfm, splits=splits)\n",
+    "dls = tls.dataloaders(after_item=[Resize(224), ToTensor], \n",
+    "    after_batch=[IntToFloatTensor, Normalize.from_stats(*imagenet_stats)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's go back to the input pipeline we set up in <<chapter_midlevel_data>> for a Siamese network. If your remember, it consisted of pair of images with the label being `True` or `False`, depending on if they were in the same class or not.\n",
+    "\n",
+    "Using what we just saw, let's build a custom model for this task and train it. How? We will use a pretrained architecture and pass our two images throught it. Then we can concatenate the results and send them to a custom head that will return two predictions. In terms of modules, this looks like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SiameseModel(Module):\n",
+    "    def __init__(self, encoder, head):\n",
+    "        self.encoder,self.head = encoder,head\n",
+    "    \n",
+    "    def forward(self, x1, x2):\n",
+    "        ftrs = torch.cat([self.encoder(x1), self.encoder(x2)], dim=1)\n",
+    "        return self.head(ftrs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create our encoder, we just need to take a pretrained model and cut it, as we explained before. The function `create_body` does that for us, we just have to pass it the place we want to cut. If we remember our look in the dictionary of metadata for pretrained models, the cut value for a resnet is -2:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoder = create_body(resnet34, cut=-2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we can create our head. A look at the encoder tells us the last layer has 512 features, so this head will need to receive `512*4`. Why 4? First we have to multiply by 2 because we have two images. Then we need a second multiplication by 2 because of our concat-pool trick."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "head = create_head(512*4, 2, ps=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With our encoder and head, we can now build our model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = SiameseModel(encoder, head)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before using `Learner`, we have two more things to define. First, we must define the loss function we want to use. It's regular cross entropy, but since our targets are booleans, we need to convert them to integers or PyTorch will throw an error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def loss_func(out, targ):\n",
+    "    return nn.CrossEntropyLoss()(out, targ.long())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "More importantly, to take full advantage of transfer learning, we have to define a custom *splitter*. A splitter is a function that tells the fastai library how to split the model in several parameter groups. This is what is used behind the scenes not only train the head of a model when we do transfert learning. \n",
+    "\n",
+    "Here we want two parameter groups: one for the encoder and one for the head. We can thus define the following splitter (`params` is jsut a function that returns all parameters of a given module):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def siamese_splitter(model):\n",
+    "    return [params(model.encoder), params(model.head)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we can define our `Learner` by passing the data, model, loss function, spliiter and any metric we want. Since we are not using a convenience function from fastai for transfer learning (like `cnn_learner`), we have to call `learn.freeze` manually. This will make sure only the last parameter groups (in this case, the head) is trained. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn = Learner(dls, model, loss_func=loss_func, \n",
+    "                splitter=siamese_splitter, metrics=accuracy)\n",
+    "learn.freeze()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we can directly train our model with the usual methods:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th>epoch</th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>valid_loss</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>0.367015</td>\n",
+       "      <td>0.281242</td>\n",
+       "      <td>0.885656</td>\n",
+       "      <td>00:26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>0.307688</td>\n",
+       "      <td>0.214721</td>\n",
+       "      <td>0.915426</td>\n",
+       "      <td>00:26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.275221</td>\n",
+       "      <td>0.170615</td>\n",
+       "      <td>0.936401</td>\n",
+       "      <td>00:26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>0.223771</td>\n",
+       "      <td>0.159633</td>\n",
+       "      <td>0.943843</td>\n",
+       "      <td>00:26</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "learn.fit_one_cycle(4, 3e-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before unfreezing and training a bit more with discriminative learning rates..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th>epoch</th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>valid_loss</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>0.212744</td>\n",
+       "      <td>0.159033</td>\n",
+       "      <td>0.944520</td>\n",
+       "      <td>00:35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>0.201893</td>\n",
+       "      <td>0.159615</td>\n",
+       "      <td>0.942490</td>\n",
+       "      <td>00:35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.204606</td>\n",
+       "      <td>0.152338</td>\n",
+       "      <td>0.945196</td>\n",
+       "      <td>00:36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>0.213203</td>\n",
+       "      <td>0.148346</td>\n",
+       "      <td>0.947903</td>\n",
+       "      <td>00:36</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "learn.unfreeze()\n",
+    "learn.fit_one_cycle(4, slice(1e-6,1e-4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "94.8% is very good when we remember a classifier trained the same way (with no data augmentation) had an arror rate of 7%."
    ]
   },
   {
@@ -476,6 +810,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

16_accel_sgd.ipynb

@@ -1307,6 +1307,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

17_foundations.ipynb

@@ -2037,7 +2037,8 @@
     "        return self.out\n",
     "    \n",
     "    def backward(self):\n",
-    "        self.inp.g = 2. * (self.inp.squeeze() - self.targ).unsqueeze(-1) / self.targ.shape[0]"
+    "        x = (self.inp.squeeze()-self.targ).unsqueeze(-1)\n",
+    "        self.inp.g = 2.*x/self.targ.shape[0]"
    ]
   },
   {
@@ -2189,7 +2190,8 @@
    "source": [
     "class Mse(LayerFunction):\n",
     "    def forward (self, inp, targ): return (inp.squeeze() - targ).pow(2).mean()\n",
-    "    def bwd(self, out, inp, targ): inp.g = 2*(inp.squeeze()-targ).unsqueeze(-1) / targ.shape[0]"
+    "    def bwd(self, out, inp, targ): \n",
+    "        inp.g = 2*(inp.squeeze()-targ).unsqueeze(-1) / targ.shape[0]"
    ]
   },
   {
@@ -2446,6 +2448,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

18_CAM.ipynb

@@ -653,6 +653,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

19_learner.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,6 +26,13 @@
     "The end of chapter questionnaire is particularly important for this chapter. This is where we will be getting you started on the many interesting directions that you could take, using this chapter as your starting out point. What we are really saying is: follow through with this chapter on your computer, not on paper, and do lots of experiments, web searches, and whatever else you need to understand what's going on. You've built up the skills and expertise to do this in the rest of this book, so we think you are going to go great!"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First let's start with gathering (manually) the data."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -42,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -67,7 +74,7 @@
        "Path('/home/jhoward/.fastai/data/imagenette2-160/val/n03417042/n03417042_3752.JPEG')"
       ]
      },
-     "execution_count": 3,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -86,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -95,7 +102,7 @@
        "Path('/home/jhoward/.fastai/data/imagenette2-160/val/n03417042/n03417042_3752.JPEG')"
       ]
      },
-     "execution_count": 4,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -117,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -127,7 +134,7 @@
        "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=213x160 at 0x7FA45AC27D50>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -139,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -148,7 +155,7 @@
        "torch.Size([160, 213, 3])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -167,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -176,7 +183,7 @@
        "(#10) ['n03417042','n03445777','n03888257','n03394916','n02979186','n03000684','n03425413','n01440764','n03028079','n02102040']"
       ]
      },
-     "execution_count": 7,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -194,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -212,7 +219,7 @@
        " 'n02102040': 9}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -225,14 +232,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "That's all the pieces we need to put together our `Dataset`:"
+    "That's all the pieces we need to put together our `Dataset`."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Dataset"
+    "### Dataset"
    ]
   },
   {
@@ -244,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -266,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -275,7 +282,7 @@
        "(9469, 3925)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -295,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -304,7 +311,7 @@
        "(torch.Size([64, 64, 3]), tensor(0))"
       ]
      },
-     "execution_count": 11,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -317,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -346,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -364,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -373,7 +380,7 @@
        "(torch.Size([2, 64, 64, 3]), tensor([0, 0]))"
       ]
      },
-     "execution_count": 14,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -392,7 +399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -419,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -428,7 +435,7 @@
        "(torch.Size([128, 64, 64, 3]), torch.Size([128]), 74)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -452,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -461,7 +468,7 @@
        "[tensor([0.4544, 0.4453, 0.4141]), tensor([0.2812, 0.2766, 0.2981])]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -480,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -501,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -511,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -520,7 +527,7 @@
        "(tensor([0.3732, 0.4907, 0.5633]), tensor([1.0212, 1.0311, 1.0131]))"
       ]
      },
-     "execution_count": 20,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -537,6 +544,13 @@
     "Here `tfm_x` isn't just applying `Normalize`, but is also permuting the axis order from `NHWC` to `NCHW` (see <<chapter_convolutions>> if you need a reminder what these acronyms refer to). PIL uses `HWC` axis order, which we can't use with PyTorch, hence the need for this `permute`."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's all we need for the data for our model. So now we need the model itself!"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -548,12 +562,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "That's all we need for the data for our model. So now we need the model itself! To create a model, we'll need `Module`. To create `Module`, we'll need `Parameter`, so let's start there. Recall that in <<chapter_collab>> we said that `Parameter` \"this class doesn't actually add any functionality (other than automatically calling `requires_grad_()` for us). It's only used as a 'marker' to show what to include in `parameters()`\". Here's a definition which does exactly that:"
+    "To create a model, we'll need `Module`. To create `Module`, we'll need `Parameter`, so let's start there. Recall that in <<chapter_collab>> we said that `Parameter` \"this class doesn't actually add any functionality (other than automatically calling `requires_grad_()` for us). It's only used as a 'marker' to show what to include in `parameters()`\". Here's a definition which does exactly that:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -573,7 +587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -582,7 +596,7 @@
        "tensor(3., requires_grad=True)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -600,7 +614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -658,7 +672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -686,7 +700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -695,7 +709,7 @@
        "2"
       ]
      },
-     "execution_count": 25,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -714,7 +728,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -723,7 +737,7 @@
        "torch.Size([128, 4, 64, 64])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -743,7 +757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -766,7 +780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -775,7 +789,7 @@
        "torch.Size([3, 2])"
       ]
      },
-     "execution_count": 28,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -795,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -814,7 +828,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -823,7 +837,7 @@
        "4"
       ]
      },
-     "execution_count": 30,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -842,7 +856,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -851,7 +865,7 @@
        "device(type='cuda', index=5)"
       ]
      },
-     "execution_count": 31,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -865,7 +879,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Simple CNN"
+    "We can now use those pieces to create a CNN."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple CNN"
    ]
   },
   {
@@ -877,7 +898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -915,7 +936,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -932,7 +953,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -956,7 +977,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -965,7 +986,7 @@
        "10"
       ]
      },
-     "execution_count": 35,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -984,7 +1005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1003,7 +1024,7 @@
        "torch.Size([128, 10])"
       ]
      },
-     "execution_count": 36,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1016,6 +1037,13 @@
     "r.shape"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have data and model. Now we need a loss function."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1027,12 +1055,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We have data and model. Now we need a loss function. We've already seen how to define \"negative log likelihood\":"
+    "We've already seen how to define \"negative log likelihood\":"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1048,7 +1076,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1057,7 +1085,7 @@
        "tensor(-1.2790, grad_fn=<SelectBackward>)"
       ]
      },
-     "execution_count": 38,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1077,7 +1105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1086,7 +1114,7 @@
        "tensor(2.5666, grad_fn=<NegBackward>)"
       ]
      },
-     "execution_count": 39,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1109,7 +1137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1118,7 +1146,7 @@
        "tensor(-1.2790, grad_fn=<SelectBackward>)"
       ]
      },
-     "execution_count": 40,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1144,7 +1172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1153,7 +1181,7 @@
        "tensor(True)"
       ]
      },
-     "execution_count": 41,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1173,7 +1201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1182,7 +1210,7 @@
        "tensor(3.9784, grad_fn=<SelectBackward>)"
       ]
      },
-     "execution_count": 42,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1204,7 +1232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1220,7 +1248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1229,7 +1257,7 @@
        "tensor(-1.2790, grad_fn=<SelectBackward>)"
       ]
      },
-     "execution_count": 44,
+     "execution_count": null,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1247,13 +1275,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "def cross_entropy(preds, yb): return nll(log_softmax(preds), yb).mean()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's now combine all those pieces together to create a `Learner`."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1270,7 +1305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1291,7 +1326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1310,12 +1345,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#id class_learner\n",
-    "#caption The Learner class\n",
     "class Learner:\n",
     "    def __init__(self, model, dls, loss_func, lr, cbs, opt_func=SGD):\n",
     "        store_attr(self, 'model,dls,loss_func,lr,cbs,opt_func')\n",
@@ -1375,7 +1408,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Callbacks"
+    "### Callbacks"
    ]
   },
   {
@@ -1391,7 +1424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1407,7 +1440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1430,7 +1463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1460,7 +1493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1508,14 +1541,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It's quite amazing to realize that we can implement all the key ideas from fastai's `Learner` in so little code!"
+    "It's quite amazing to realize that we can implement all the key ideas from fastai's `Learner` in so little code! Let's now add some learning rate scheduling."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Annealing"
+    "### Scheduling the learning rate"
    ]
   },
   {
@@ -1527,7 +1560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1556,7 +1589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1719,7 +1752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1756,6 +1789,13 @@
     "plt.plot(onecyc.lrs);"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "TK conclusion"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1841,6 +1881,9 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "split_at_heading": true
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -1856,20 +1899,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

20_conclusion.ipynb

@@ -69,6 +69,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

app_blog.ipynb

@@ -292,6 +292,18 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

app_jupyter.ipynb

@@ -533,31 +533,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.5"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
   }
  },
  "nbformat": 4,

clean/03_ethics.ipynb

@@ -0,0 +1,261 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Ethics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sidebar: Acknowledgement: Dr Rachel Thomas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### End sidebar"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Key examples for data ethics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bugs and recourse: Buggy algorithm used for healthcare benefits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Feedback loops: YouTube's recommendation system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bias: Professor Lantanya Sweeney \"arrested\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Why does this matter?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Integrating machine learning with product design"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Topics in Data Ethics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Recourse and accountability"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Feedback loops"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Historical bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Measurement bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Aggregation Bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Representation Bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Addressing different types of bias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Humans are biased, so does algorithmic bias matter?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Disinformation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Identifying and addressing ethical issues"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analyze a project you are working on"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Processes to implement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Ethical Lenses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The power of diversity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fairness, accountability, and transparency"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Role of Policy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The effectiveness of regulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rights and policy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cars: a historical precedent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Questionnaire"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Further research:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Section 1: that's a wrap!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "split_at_heading": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

clean/15_arch_details.ipynb

@@ -0,0 +1,421 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#hide\n",
+    "from utils import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Application architectures deep dive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Computer vision"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### cnn_learner"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'cut': -2,\n",
+       " 'split': <function fastai2.vision.learner._resnet_split(m)>,\n",
+       " 'stats': ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])}"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_meta[resnet50]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sequential(\n",
+       "  (0): AdaptiveConcatPool2d(\n",
+       "    (ap): AdaptiveAvgPool2d(output_size=1)\n",
+       "    (mp): AdaptiveMaxPool2d(output_size=1)\n",
+       "  )\n",
+       "  (1): full: False\n",
+       "  (2): BatchNorm1d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "  (3): Dropout(p=0.25, inplace=False)\n",
+       "  (4): Linear(in_features=20, out_features=512, bias=False)\n",
+       "  (5): ReLU(inplace=True)\n",
+       "  (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+       "  (7): Dropout(p=0.5, inplace=False)\n",
+       "  (8): Linear(in_features=512, out_features=2, bias=False)\n",
+       ")"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "create_head(20,2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### unet_learner"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A Siamese network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#hide\n",
+    "from fastai2.vision.all import *\n",
+    "path = untar_data(URLs.PETS)\n",
+    "files = get_image_files(path/\"images\")\n",
+    "\n",
+    "class SiameseImage(Tuple):\n",
+    "    def show(self, ctx=None, **kwargs): \n",
+    "        img1,img2,same_breed = self\n",
+    "        if not isinstance(img1, Tensor):\n",
+    "            if img2.size != img1.size: img2 = img2.resize(img1.size)\n",
+    "            t1,t2 = tensor(img1),tensor(img2)\n",
+    "            t1,t2 = t1.permute(2,0,1),t2.permute(2,0,1)\n",
+    "        else: t1,t2 = img1,img2\n",
+    "        line = t1.new_zeros(t1.shape[0], t1.shape[1], 10)\n",
+    "        return show_image(torch.cat([t1,line,t2], dim=2), \n",
+    "                          title=same_breed, ctx=ctx)\n",
+    "    \n",
+    "def label_func(fname):\n",
+    "    return re.match(r'^(.*)_\\d+.jpg$', fname.name).groups()[0]\n",
+    "\n",
+    "class SiameseTransform(Transform):\n",
+    "    def __init__(self, files, label_func, splits):\n",
+    "        self.labels = files.map(label_func).unique()\n",
+    "        self.lbl2files = {l: L(f for f in files if label_func(f) == l) for l in self.labels}\n",
+    "        self.label_func = label_func\n",
+    "        self.valid = {f: self._draw(f) for f in files[splits[1]]}\n",
+    "        \n",
+    "    def encodes(self, f):\n",
+    "        f2,t = self.valid.get(f, self._draw(f))\n",
+    "        img1,img2 = PILImage.create(f),PILImage.create(f2)\n",
+    "        return SiameseImage(img1, img2, t)\n",
+    "    \n",
+    "    def _draw(self, f):\n",
+    "        same = random.random() < 0.5\n",
+    "        cls = self.label_func(f)\n",
+    "        if not same: cls = random.choice(L(l for l in self.labels if l != cls)) \n",
+    "        return random.choice(self.lbl2files[cls]),same\n",
+    "    \n",
+    "splits = RandomSplitter()(files)\n",
+    "tfm = SiameseTransform(files, label_func, splits)\n",
+    "tls = TfmdLists(files, tfm, splits=splits)\n",
+    "dls = tls.dataloaders(after_item=[Resize(224), ToTensor], \n",
+    "    after_batch=[IntToFloatTensor, Normalize.from_stats(*imagenet_stats)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SiameseModel(Module):\n",
+    "    def __init__(self, encoder, head):\n",
+    "        self.encoder,self.head = encoder,head\n",
+    "    \n",
+    "    def forward(self, x1, x2):\n",
+    "        ftrs = torch.cat([self.encoder(x1), self.encoder(x2)], dim=1)\n",
+    "        return self.head(ftrs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoder = create_body(resnet34, cut=-2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "head = create_head(512*4, 2, ps=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = SiameseModel(encoder, head)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def loss_func(out, targ):\n",
+    "    return nn.CrossEntropyLoss()(out, targ.long())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def siamese_splitter(model):\n",
+    "    return [params(model.encoder), params(model.head)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn = Learner(dls, model, loss_func=loss_func, \n",
+    "                splitter=siamese_splitter, metrics=accuracy)\n",
+    "learn.freeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th>epoch</th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>valid_loss</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>0.367015</td>\n",
+       "      <td>0.281242</td>\n",
+       "      <td>0.885656</td>\n",
+       "      <td>00:26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>0.307688</td>\n",
+       "      <td>0.214721</td>\n",
+       "      <td>0.915426</td>\n",
+       "      <td>00:26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.275221</td>\n",
+       "      <td>0.170615</td>\n",
+       "      <td>0.936401</td>\n",
+       "      <td>00:26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>0.223771</td>\n",
+       "      <td>0.159633</td>\n",
+       "      <td>0.943843</td>\n",
+       "      <td>00:26</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "learn.fit_one_cycle(4, 3e-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th>epoch</th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>valid_loss</th>\n",
+       "      <th>accuracy</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>0.212744</td>\n",
+       "      <td>0.159033</td>\n",
+       "      <td>0.944520</td>\n",
+       "      <td>00:35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>0.201893</td>\n",
+       "      <td>0.159615</td>\n",
+       "      <td>0.942490</td>\n",
+       "      <td>00:35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.204606</td>\n",
+       "      <td>0.152338</td>\n",
+       "      <td>0.945196</td>\n",
+       "      <td>00:36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>0.213203</td>\n",
+       "      <td>0.148346</td>\n",
+       "      <td>0.947903</td>\n",
+       "      <td>00:36</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "learn.unfreeze()\n",
+    "learn.fit_one_cycle(4, slice(1e-6,1e-4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Natural language processing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tabular"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Wrapping up architectures"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Questionnaire"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Further research"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "split_at_heading": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

clean/20_conclusion.ipynb

@@ -0,0 +1,42 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Concluding thoughts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "split_at_heading": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

clean/app_blog.ipynb

@@ -0,0 +1,95 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#hide\n",
+    "from utils import *\n",
+    "from fastai2.vision.widgets import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating a blog"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Blogging with GitHub Pages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating the repository"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting up your homepage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating posts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Synchronizing GitHub and your computer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Jupyter for blogging"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "split_at_heading": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

clean/images

@@ -0,0 +1 @@
+../images