diff --git a/Ch10-deeplearning-lab.Rmd b/Ch10-deeplearning-lab.Rmd
index e6911cc..51674b6 100644
--- a/Ch10-deeplearning-lab.Rmd
+++ b/Ch10-deeplearning-lab.Rmd
@@ -1,19 +1,3 @@
----
-jupyter:
-  jupytext:
-    cell_metadata_filter: -all
-    formats: ipynb,Rmd
-    text_representation:
-      extension: .Rmd
-      format_name: rmarkdown
-      format_version: '1.2'
-      jupytext_version: 1.14.7
-  kernelspec:
-    display_name: Python 3 (ipykernel)
-    language: python
-    name: python3
----
-
 
 # Chapter 10
 
@@ -872,7 +856,7 @@ for idx, (X_ ,Y_) in enumerate(cifar_dm.train_dataloader()):
 
 
 Before we start, we look at some of the training images; similar code produced
-Figure 10.5 on page  164. The example below also illustrates
+Figure 10.5 on page  447. The example below also illustrates
 that `TensorDataset` objects can be indexed with integers --- we are choosing
 random images from the training data by indexing `cifar_train`. In order to display correctly,
 we must reorder the dimensions by a call to `np.transpose()`.
@@ -1705,7 +1689,6 @@ early stopping, since then the test performance would be biased.
 
 We form the training dataset similar to
 our `Hitters` example.
- 
 
 ```{python}
 datasets = []
diff --git a/Ch10-deeplearning-lab.ipynb b/Ch10-deeplearning-lab.ipynb
index 835512f..2577eac 100644
--- a/Ch10-deeplearning-lab.ipynb
+++ b/Ch10-deeplearning-lab.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b672dcf6",
+   "id": "23016bca",
    "metadata": {},
    "source": [
     "\n",
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "9b5b8319",
+   "id": "cf431f3f",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c0cb2c87",
+   "id": "667eff82",
    "metadata": {},
    "source": [
     "### Torch-Specific Imports\n",
@@ -61,7 +61,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "f66f362c",
+   "id": "1db00e03",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ce27a8d",
+   "id": "b3407495",
    "metadata": {},
    "source": [
     "There are several other helper packages for `torch`. For instance,\n",
@@ -87,7 +87,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "17bce9cb",
+   "id": "3da0a445",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a14990c3",
+   "id": "e5c55b19",
    "metadata": {},
    "source": [
     "The package `pytorch_lightning` is a somewhat higher-level\n",
@@ -111,7 +111,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "4c3b6e43",
+   "id": "bbbf32fe",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5bc78bd",
+   "id": "cf5ec401",
    "metadata": {},
    "source": [
     "In order to reproduce results we use `seed_everything()`. We will also instruct `torch` to use deterministic algorithms\n",
@@ -131,7 +131,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "6839d8ad",
+   "id": "3810caf4",
    "metadata": {},
    "outputs": [
     {
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f1490e7",
+   "id": "c3dea945",
    "metadata": {},
    "source": [
     "We will use several datasets shipped with `torchvision` for our\n",
@@ -161,7 +161,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "07dba2dd",
+   "id": "454dc419",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -179,7 +179,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36ec305c",
+   "id": "f7f9578a",
    "metadata": {},
    "source": [
     "We have provided a few utilities in `ISLP` specifically for this lab.\n",
@@ -197,7 +197,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "89763447",
+   "id": "cd43a4c6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dfc13283",
+   "id": "c5231b9d",
    "metadata": {},
    "source": [
     "In addition we have included some helper\n",
@@ -226,7 +226,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "ac5ab1b0",
+   "id": "eaf84e9c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -238,7 +238,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "04f757bd",
+   "id": "c42bc542",
    "metadata": {},
    "source": [
     "Finally, we introduce some utility imports  not directly related to\n",
@@ -255,7 +255,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "5a5468e6",
+   "id": "d007a49b",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -267,7 +267,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "690a5255",
+   "id": "d0fe1cff",
    "metadata": {},
    "source": [
     "## Single Layer Network on Hitters Data\n",
@@ -277,7 +277,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "d635398b",
+   "id": "9da64364",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -289,7 +289,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0fd86f3",
+   "id": "0e80a8c0",
    "metadata": {},
    "source": [
     " We will fit two linear models (least squares  and lasso) and  compare their performance\n",
@@ -305,7 +305,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "2c621749",
+   "id": "a2cfe999",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -318,7 +318,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f32a66d7",
+   "id": "5f0851bc",
    "metadata": {},
    "source": [
     "The `to_numpy()`  method above converts `pandas`\n",
@@ -331,7 +331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5565350",
+   "id": "afe4eb28",
    "metadata": {},
    "source": [
     "We now split the data into test and training, fixing the random\n",
@@ -341,7 +341,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "cd19596a",
+   "id": "5c600069",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -356,7 +356,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b82951aa",
+   "id": "e27a6313",
    "metadata": {},
    "source": [
     "### Linear Models\n",
@@ -366,7 +366,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "e4bfcd5a",
+   "id": "6ea4f551",
    "metadata": {},
    "outputs": [
     {
@@ -388,7 +388,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf3523a8",
+   "id": "e5fe8c6b",
    "metadata": {},
    "source": [
     "Next we fit the lasso using `sklearn`. We are using\n",
@@ -402,7 +402,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "36f8290b",
+   "id": "f1b8b3f5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -414,7 +414,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f1bf0ad",
+   "id": "39a58ed0",
    "metadata": {},
    "source": [
     "We need to create a grid of values for $\\lambda$. As is common practice, \n",
@@ -425,7 +425,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "5015c394",
+   "id": "50ce4171",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -440,7 +440,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "89f6b4e4",
+   "id": "2cdc3810",
    "metadata": {},
    "source": [
     "Note that we had to transform the data first, since the scale of the variables impacts the choice of $\\lambda$.\n",
@@ -450,7 +450,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "b5dc7d68",
+   "id": "94c4ab75",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -466,7 +466,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b64a882",
+   "id": "e5262d51",
    "metadata": {},
    "source": [
     "We extract the lasso model with best cross-validated mean absolute error, and evaluate its\n",
@@ -477,7 +477,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "2c49196f",
+   "id": "86e45999",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -501,7 +501,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61f016c9",
+   "id": "1f905d89",
    "metadata": {},
    "source": [
     "This is similar to the results we got for the linear model fit by least squares. However, these results can vary a lot for different train/test splits; we encourage the reader to try a different seed in code block 12 and rerun the subsequent code up to this point.\n",
@@ -519,7 +519,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "df5864e2",
+   "id": "00ac7606",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -541,7 +541,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4b05cc28",
+   "id": "a25aab3b",
    "metadata": {},
    "source": [
     "The `class` statement identifies the code chunk as a\n",
@@ -577,7 +577,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "da601fe1",
+   "id": "bb7ff7e9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -586,7 +586,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "326f3b54",
+   "id": "ac0c6bf7",
    "metadata": {},
    "source": [
     "The object `self.sequential` is a composition of four maps. The\n",
@@ -601,7 +601,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a246aedb",
+   "id": "9e8e69ac",
    "metadata": {},
    "source": [
     "The package `torchinfo` provides a `summary()` function that neatly summarizes\n",
@@ -612,7 +612,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "15dd23a9",
+   "id": "b60d34e1",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -658,7 +658,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3cb4b8bc",
+   "id": "aa499e3f",
    "metadata": {},
    "source": [
     "We have truncated the end of the output slightly, here and in subsequent uses.\n",
@@ -680,7 +680,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "dae83bc5",
+   "id": "42f63682",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -693,7 +693,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80475fa2",
+   "id": "6e184dcd",
    "metadata": {},
    "source": [
     "We do the same for the test data."
@@ -702,7 +702,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "81e217a8",
+   "id": "57fbf564",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -713,7 +713,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f49263d",
+   "id": "09cabaa8",
    "metadata": {},
    "source": [
     "Finally, this dataset is passed to a `DataLoader()` which ultimately\n",
@@ -737,7 +737,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "e5359e31",
+   "id": "570bdd73",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -746,7 +746,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "105b015f",
+   "id": "a7bc2151",
    "metadata": {},
    "source": [
     "The general training setup in `pytorch_lightning` involves\n",
@@ -769,7 +769,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "7a19d6d8",
+   "id": "c08a4d6d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -782,7 +782,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d1f2a76",
+   "id": "db5447fa",
    "metadata": {},
    "source": [
     "Next we must provide a `pytorch_lightning` module that controls\n",
@@ -797,7 +797,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "07bc10ef",
+   "id": "aaa1e593",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -807,7 +807,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ebf9835",
+   "id": "8500a2ba",
    "metadata": {},
    "source": [
     " By using the `SimpleModule.regression()` method,  we indicate that we will use squared-error loss as in\n",
@@ -824,7 +824,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "08c71fb4",
+   "id": "1a4e9b3c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -833,7 +833,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "511617c3",
+   "id": "77e3c7a5",
    "metadata": {},
    "source": [
     "Finally we are ready to train our model and log the results. We\n",
@@ -855,7 +855,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "81a8c626",
+   "id": "2f839fde",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -897,7 +897,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4cb0d8941d43434883b4142c14e198f8",
+       "model_id": "3f81045e13e641428a7f37ab7ceb43be",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1569,7 +1569,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "840b55ce195b42edbe9f3fd394dec7e5",
+       "model_id": "553e586e7cd54ad3bb9e01d0fc37754e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1583,7 +1583,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2e3d175f2c294fac992ff04733267452",
+       "model_id": "05c22b9bdd4c48098756a37b57fc963b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1597,7 +1597,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2d0477c9d13945bb8a6cbd38620bc93f",
+       "model_id": "f073bf03d90b4e318352c5de82bb9953",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1627,7 +1627,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "abe1a22c",
+   "id": "4018f616",
    "metadata": {},
    "source": [
     "At each step of SGD, the algorithm randomly selects 32 training observations for\n",
@@ -1643,7 +1643,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "083670c6",
+   "id": "672b4410",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -1651,7 +1651,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ddc1a0eaac9e4fcda9e91e34abdcc67d",
+       "model_id": "f0857a2a00a847c084831c51d7fd8dfd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1704,7 +1704,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6b3317b",
+   "id": "0f4e3b11",
    "metadata": {},
    "source": [
     "The results of the fit have been logged into a CSV file. We can find the\n",
@@ -1720,7 +1720,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "02ba9edf",
+   "id": "8cf6ef60",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1729,7 +1729,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "daf8ed8d",
+   "id": "537efe03",
    "metadata": {},
    "source": [
     "Since we will produce similar plots in later examples, we write a\n",
@@ -1739,7 +1739,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "4184557c",
+   "id": "67ce1e26",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -1773,7 +1773,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "026c1c4d",
+   "id": "53309bb0",
    "metadata": {},
    "source": [
     "We now set up our axes, and use our function to produce the MAE plot."
@@ -1782,7 +1782,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "3244deeb",
+   "id": "deb684d2",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -1811,7 +1811,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "670a3e8f",
+   "id": "eab05619",
    "metadata": {},
    "source": [
     "We can predict directly from the final model, and\n",
@@ -1829,7 +1829,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "36bb545e",
+   "id": "454033dd",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -1853,7 +1853,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae4b9ca6",
+   "id": "b3625ff5",
    "metadata": {},
    "source": [
     "    "
@@ -1861,7 +1861,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8b7fd3c",
+   "id": "f46e3883",
    "metadata": {},
    "source": [
     "### Cleanup\n",
@@ -1875,7 +1875,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "00371f48",
+   "id": "71b3d0d0",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -1894,7 +1894,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5707d61c",
+   "id": "28d1c832",
    "metadata": {},
    "source": [
     "## Multilayer Network on the MNIST Digit Data\n",
@@ -1908,7 +1908,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "3e28d6ba",
+   "id": "def8605c",
    "metadata": {},
    "outputs": [
     {
@@ -1939,7 +1939,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb8b7e29",
+   "id": "95ffc346",
    "metadata": {},
    "source": [
     "There are 60,000 images in the training data and 10,000 in the test\n",
@@ -1963,7 +1963,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "cb04829e",
+   "id": "8b9e2b8c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1976,7 +1976,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1119e22a",
+   "id": "91256a1b",
    "metadata": {},
    "source": [
     "Let’s take a look at the data that will get fed into our network. We loop through the first few\n",
@@ -1986,7 +1986,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "c4a265fc",
+   "id": "a4b95dc6",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -2012,7 +2012,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f65ada90",
+   "id": "12e7eddb",
    "metadata": {},
    "source": [
     "We see that the $X$ for each batch consists of 256 images of size `1x28x28`.\n",
@@ -2025,7 +2025,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "60339a03",
+   "id": "17714c25",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2051,7 +2051,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b8f87da",
+   "id": "9893ffb2",
    "metadata": {},
    "source": [
     "We see that in the first layer, each `1x28x28` image is flattened, then mapped to\n",
@@ -2065,7 +2065,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "1d0f24b2",
+   "id": "88a4bf46",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2074,7 +2074,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0ee4771e",
+   "id": "049febff",
    "metadata": {},
    "source": [
     "We can check that the model produces output of expected size based\n",
@@ -2084,7 +2084,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "42a4931b",
+   "id": "ea0d9387",
    "metadata": {},
    "outputs": [
     {
@@ -2104,7 +2104,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "628d56f9",
+   "id": "638026d1",
    "metadata": {},
    "source": [
     "Let’s take a look at the summary of the model. Instead of an `input_size` we can pass\n",
@@ -2115,7 +2115,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "69e62d88",
+   "id": "17c34a29",
    "metadata": {},
    "outputs": [
     {
@@ -2164,7 +2164,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f8e77337",
+   "id": "73e3cd00",
    "metadata": {},
    "source": [
     "Having set up both  the model and the data module, fitting this model is\n",
@@ -2177,7 +2177,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "2027a378",
+   "id": "a0608bd1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2188,7 +2188,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9146b9b5",
+   "id": "6959c893",
    "metadata": {},
    "source": [
     "Now we are ready to go. The final step is to supply training data, and fit the model."
@@ -2197,7 +2197,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "4a5e941d",
+   "id": "cf8e3d9d",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -2239,7 +2239,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "23f1384f37824cc59404e8a856f4962d",
+       "model_id": "2c7fc7b3fc61455b88cf7020ce62d19e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2689,7 +2689,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8099bdc9",
+   "id": "cc8724fa",
    "metadata": {},
    "source": [
     "We have suppressed the output here, which is a progress report on the\n",
@@ -2707,7 +2707,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d6c3bbbc",
+   "id": "b9dc38ac",
    "metadata": {},
    "source": [
     "`SimpleModule.classification()` includes\n",
@@ -2720,7 +2720,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "603a278a",
+   "id": "45e03385",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -2750,7 +2750,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "654cec05",
+   "id": "1679f357",
    "metadata": {},
    "source": [
     "Once again we evaluate the accuracy using the `test()` method of our trainer. This model achieves\n",
@@ -2760,13 +2760,13 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "93dc968b",
+   "id": "3a875b78",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3eeaa8a87a31475bb36fe8dd24e694d9",
+       "model_id": "759de3284ee24f5c8191c1fc922f3e1d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2820,7 +2820,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f7dc344",
+   "id": "12ef4787",
    "metadata": {},
    "source": [
     "Table 10.1 also reports the error rates resulting from LDA (Chapter 4) and multiclass logistic\n",
@@ -2834,7 +2834,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "e1975a3e",
+   "id": "2f035d83",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2855,7 +2855,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "14c79199",
+   "id": "2cd67ad8",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -2899,7 +2899,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9ecb4b2d1cba45a9b762950d7ad365e4",
+       "model_id": "c932821add0e4296a5ff2122a94b6090",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3347,7 +3347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f84cfa36",
+   "id": "20a316a1",
    "metadata": {},
    "source": [
     "We fit the model just as before and compute the test results."
@@ -3356,7 +3356,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "acb6f88d",
+   "id": "3900d16e",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -3364,7 +3364,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "96180813745947aa8b3705fa737d63a0",
+       "model_id": "63d666ec843544bf804c3fc6fc5fd6ab",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3418,7 +3418,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05244a5e",
+   "id": "ca80aa75",
    "metadata": {},
    "source": [
     "The accuracy is above 90% even for this pretty simple model.\n",
@@ -3430,7 +3430,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "f5b3f811",
+   "id": "679f2ea5",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -3450,7 +3450,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5b395bb",
+   "id": "dd8ba586",
    "metadata": {},
    "source": [
     "## Convolutional Neural Networks\n",
@@ -3461,7 +3461,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "e1caa7ac",
+   "id": "e4af6604",
    "metadata": {},
    "outputs": [
     {
@@ -3484,7 +3484,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "9e94a7b4",
+   "id": "2b613ecc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3501,7 +3501,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "219ccc6f",
+   "id": "af1d3cdc",
    "metadata": {},
    "source": [
     "The `CIFAR100` dataset consists of 50,000 training images, each represented by a three-dimensional tensor:\n",
@@ -3515,7 +3515,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "32c78c06",
+   "id": "4b325cb4",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -3530,7 +3530,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4570164",
+   "id": "f361276e",
    "metadata": {},
    "source": [
     "We again look at the shape of typical batches in our data loaders."
@@ -3539,7 +3539,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "b3c27322",
+   "id": "cb3d00cb",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -3565,11 +3565,11 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6152280",
+   "id": "08771862",
    "metadata": {},
    "source": [
     "Before we start, we look at some of the training images; similar code produced\n",
-    "Figure 10.5 on page  164. The example below also illustrates\n",
+    "Figure 10.5 on page  447. The example below also illustrates\n",
     "that `TensorDataset` objects can be indexed with integers --- we are choosing\n",
     "random images from the training data by indexing `cifar_train`. In order to display correctly,\n",
     "we must reorder the dimensions by a call to `np.transpose()`."
@@ -3578,7 +3578,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "c626e0ff",
+   "id": "60d09656",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -3611,7 +3611,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2a1c4832",
+   "id": "642140af",
    "metadata": {},
    "source": [
     "Here the `imshow()` method recognizes from the shape of its argument that it is a 3-dimensional array, with the last dimension indexing the three RGB color channels.\n",
@@ -3628,7 +3628,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "9d5bcdf3",
+   "id": "f823da11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3652,7 +3652,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a7204121",
+   "id": "00927159",
    "metadata": {},
    "source": [
     "Notice that we used the `padding = \"same\"` argument to\n",
@@ -3673,7 +3673,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "3e13e9bc",
+   "id": "1a172f7e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3700,7 +3700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b07fb1a",
+   "id": "8455079f",
    "metadata": {},
    "source": [
     "We  build the model and look at the summary. (We had created examples of `X_` earlier.)"
@@ -3709,7 +3709,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "id": "15c4a382",
+   "id": "651e62b4",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -3772,7 +3772,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b168c198",
+   "id": "7dd67ce7",
    "metadata": {},
    "source": [
     "The total number of trainable parameters is 964,516.\n",
@@ -3806,7 +3806,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "4a40238a",
+   "id": "63f2650e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3820,7 +3820,7 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "id": "8aab2c62",
+   "id": "a3e4bc28",
    "metadata": {},
    "outputs": [
     {
@@ -3860,7 +3860,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8473f6db1fdc40b7909b66fc6410277d",
+       "model_id": "40c811fa26da4690a95838e6ab0a8a98",
        "version_major": 2,
        "version_minor": 0
       },
@@ -4310,7 +4310,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "090f6400",
+   "id": "c3fc9402",
    "metadata": {},
    "source": [
     "This model can take 10 minutes or more to run and achieves about 42% accuracy on the test\n",
@@ -4326,7 +4326,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "id": "12474ef6",
+   "id": "6b161d93",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -4357,7 +4357,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3fe0cd4",
+   "id": "71dea0c8",
    "metadata": {},
    "source": [
     "Finally, we evaluate our model on our test data."
@@ -4366,7 +4366,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "id": "9d632437",
+   "id": "ab8a91dc",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -4374,7 +4374,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fc46f12820ff4ca281d8a035a53fa610",
+       "model_id": "6dced9ab160c4c30b094a877f330efba",
        "version_major": 2,
        "version_minor": 0
       },
@@ -4428,7 +4428,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4b69e259",
+   "id": "11d0e8b3",
    "metadata": {},
    "source": [
     "### Hardware Acceleration\n",
@@ -4449,7 +4449,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "52a43158",
+   "id": "6d9962ae",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -4491,7 +4491,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "efb32ed16b3946ebbdb7c6a1714c7347",
+       "model_id": "6409051044c94ad9af785a42df78d36e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -4940,7 +4940,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ae6af1d96e5a4d1fa048bbbf0a3f5b7b",
+       "model_id": "4f37d3b614314b6fbf9dfb3df1775948",
        "version_major": 2,
        "version_minor": 0
       },
@@ -4969,7 +4969,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b72de871",
+   "id": "3faab186",
    "metadata": {},
    "source": [
     "This yields approximately two- or three-fold  acceleration for each epoch.\n",
@@ -4979,7 +4979,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b86d6ef4",
+   "id": "31759785",
    "metadata": {},
    "source": [
     "## Using Pretrained CNN Models\n",
@@ -5001,7 +5001,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "id": "db2febe9",
+   "id": "a71c9acb",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -5031,7 +5031,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6696ee2",
+   "id": "89071e87",
    "metadata": {},
    "source": [
     "We now set up the trained network with the weights we read in code block~6. The model has 50 layers, with a fair bit of complexity."
@@ -5040,7 +5040,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "id": "2aabd724",
+   "id": "4f890244",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -5255,7 +5255,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c4ee6ebe",
+   "id": "185bb4b5",
    "metadata": {},
    "source": [
     "We set the mode to `eval()` to ensure that the model is ready to predict on new data."
@@ -5264,7 +5264,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "6d27342d",
+   "id": "c4be9922",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -5461,7 +5461,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3c1fbc71",
+   "id": "f0a3c519",
    "metadata": {},
    "source": [
     "Inspecting the output above, we see that when setting up the\n",
@@ -5474,7 +5474,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "efdbeda1",
+   "id": "2dc63d85",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5483,7 +5483,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38620865",
+   "id": "15ec1321",
    "metadata": {},
    "source": [
     "Let’s look at the predicted probabilities for each of the top 3 choices. First we compute\n",
@@ -5495,7 +5495,7 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "82ceab1c",
+   "id": "711d5ba7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5505,7 +5505,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e3ae755",
+   "id": "3b514c1e",
    "metadata": {},
    "source": [
     "In order to see the class labels, we must download the index file associated with `imagenet`. {This is avalable from the book website and  [s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json](https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json).}"
@@ -5514,7 +5514,7 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "id": "921ee168",
+   "id": "b22f70d8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5528,7 +5528,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9fdd7587",
+   "id": "a5812782",
    "metadata": {},
    "source": [
     "We’ll now construct a data frame for each image file\n",
@@ -5539,7 +5539,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "e7770017",
+   "id": "b19c6bd1",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -5592,7 +5592,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12862208",
+   "id": "cd6bd40b",
    "metadata": {},
    "source": [
     "We see that the model\n",
@@ -5605,7 +5605,7 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "id": "af2f9856",
+   "id": "ba80b615",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -5622,7 +5622,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d9e8d59c",
+   "id": "2e6eafaf",
    "metadata": {},
    "source": [
     "## IMDB Document Classification\n",
@@ -5649,7 +5649,7 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "afd98123",
+   "id": "ba6d2d2c",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -5676,7 +5676,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28a01855",
+   "id": "ebeeb069",
    "metadata": {},
    "source": [
     "The datasets `imdb_seq_train` and `imdb_seq_test` are\n",
@@ -5694,7 +5694,7 @@
   {
    "cell_type": "code",
    "execution_count": 71,
-   "id": "5981eb05",
+   "id": "93bda908",
    "metadata": {},
    "outputs": [
     {
@@ -5715,7 +5715,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b579cb29",
+   "id": "6de86e76",
    "metadata": {},
    "source": [
     "For our first model, we have created a binary feature for each\n",
@@ -5728,7 +5728,7 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "id": "f08024ba",
+   "id": "40943b7d",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -5746,7 +5746,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c070a319",
+   "id": "9328d75c",
    "metadata": {},
    "source": [
     "We’ll use a two-layer model for our first model."
@@ -5755,7 +5755,7 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "id": "7fd34906",
+   "id": "2117fd9f",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -5783,7 +5783,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fcc2a8af",
+   "id": "845b4d8e",
    "metadata": {},
    "source": [
     "We now instantiate our model and look at a summary."
@@ -5792,7 +5792,7 @@
   {
    "cell_type": "code",
    "execution_count": 74,
-   "id": "56f74fdb",
+   "id": "66d0b710",
    "metadata": {},
    "outputs": [
     {
@@ -5836,7 +5836,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a32aca43",
+   "id": "c8bdad40",
    "metadata": {},
    "source": [
     "We’ll again use\n",
@@ -5854,7 +5854,7 @@
   {
    "cell_type": "code",
    "execution_count": 75,
-   "id": "3da7e0bc",
+   "id": "9df8b4cf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5866,7 +5866,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "940c8342",
+   "id": "95cc3b4f",
    "metadata": {},
    "source": [
     "Having loaded the datasets into a data module\n",
@@ -5877,7 +5877,7 @@
   {
    "cell_type": "code",
    "execution_count": 76,
-   "id": "3b6de185",
+   "id": "73684c66",
    "metadata": {},
    "outputs": [
     {
@@ -5925,7 +5925,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "57e25e1e102745c18849507d8fe93fe3",
+       "model_id": "253cf3e077d845569cdc459ef74902b6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -6376,7 +6376,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5985c44a",
+   "id": "f2e03c88",
    "metadata": {},
    "source": [
     "Evaluating the test error yields roughly 86% accuracy."
@@ -6385,7 +6385,7 @@
   {
    "cell_type": "code",
    "execution_count": 77,
-   "id": "97f86a32",
+   "id": "01c6e5ff",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -6393,7 +6393,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "57a8b619aaf049b4b06550a47d5e3611",
+       "model_id": "f2b40a42021f4a9183c3d7d2d545e83b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -6447,7 +6447,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d677b24",
+   "id": "c1976042",
    "metadata": {},
    "source": [
     "### Comparison to Lasso\n",
@@ -6460,7 +6460,7 @@
   {
    "cell_type": "code",
    "execution_count": 78,
-   "id": "e36e1542",
+   "id": "4d5b9d1d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6473,7 +6473,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8216f2e7",
+   "id": "b78eb64e",
    "metadata": {},
    "source": [
     "Similar to what we did in\n",
@@ -6484,7 +6484,7 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "id": "ee6d6859",
+   "id": "e2a88e57",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -6497,7 +6497,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cf9b703f",
+   "id": "81380bee",
    "metadata": {},
    "source": [
     "With `LogisticRegression()` the regularization parameter\n",
@@ -6509,7 +6509,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "id": "35a6e3c2",
+   "id": "9a3cf7a3",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -6524,7 +6524,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "49cec308",
+   "id": "9ee15d44",
    "metadata": {},
    "source": [
     "The path of 50 values takes approximately 40 seconds to run."
@@ -6533,7 +6533,7 @@
   {
    "cell_type": "code",
    "execution_count": 81,
-   "id": "a8943a6a",
+   "id": "b46f02c2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6549,7 +6549,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f20738d",
+   "id": "eb3e3871",
    "metadata": {},
    "source": [
     "The coefficient and intercepts have an extraneous dimension which can be removed\n",
@@ -6559,7 +6559,7 @@
   {
    "cell_type": "code",
    "execution_count": 82,
-   "id": "08268ae5",
+   "id": "e5fb6afa",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -6571,7 +6571,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94991ca8",
+   "id": "f66fa37c",
    "metadata": {},
    "source": [
     "We’ll now make a plot to compare our neural network results with the\n",
@@ -6581,7 +6581,7 @@
   {
    "cell_type": "code",
    "execution_count": 83,
-   "id": "e98f4506",
+   "id": "cad28f1a",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -6613,7 +6613,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aac5c38c",
+   "id": "c58ff7cb",
    "metadata": {},
    "source": [
     "Notice the use of `%%capture`, which suppresses the displaying of the partially completed figure. This is useful\n",
@@ -6624,7 +6624,7 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "id": "6925d53a",
+   "id": "a66ecdd8",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -6660,7 +6660,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2a8b7736",
+   "id": "fa1e2550",
    "metadata": {},
    "source": [
     "From the graphs we see that the accuracy of the lasso logistic regression peaks at about $0.88$,  as it does for  the neural network.\n",
@@ -6671,7 +6671,7 @@
   {
    "cell_type": "code",
    "execution_count": 85,
-   "id": "6d1f7885",
+   "id": "62440c1c",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -6687,7 +6687,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f79c23a",
+   "id": "fe67520f",
    "metadata": {},
    "source": [
     "## Recurrent Neural Networks\n",
@@ -6697,7 +6697,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d376496",
+   "id": "a9bcdd2a",
    "metadata": {},
    "source": [
     "### Sequential Models for Document Classification\n",
@@ -6717,7 +6717,7 @@
   {
    "cell_type": "code",
    "execution_count": 86,
-   "id": "b5c2b72c",
+   "id": "c73d6e28",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6731,7 +6731,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d1b3ef1",
+   "id": "1edd3143",
    "metadata": {},
    "source": [
     "The first layer of the RNN is an embedding layer of size 32, which will be\n",
@@ -6748,7 +6748,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe55c7fa",
+   "id": "b73e08ab",
    "metadata": {},
    "source": [
     "The second  layer is an LSTM with 32 units, and the output\n",
@@ -6760,7 +6760,7 @@
   {
    "cell_type": "code",
    "execution_count": 87,
-   "id": "8985cdb1",
+   "id": "cc9bbd00",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -6781,7 +6781,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78fc988d",
+   "id": "5e9f28dd",
    "metadata": {},
    "source": [
     "We instantiate and take a look at the summary of the model, using the\n",
@@ -6791,7 +6791,7 @@
   {
    "cell_type": "code",
    "execution_count": 88,
-   "id": "79184187",
+   "id": "5c9ffb46",
    "metadata": {},
    "outputs": [
     {
@@ -6833,7 +6833,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d5b2c85e",
+   "id": "5087ff10",
    "metadata": {},
    "source": [
     "The 10,003 is suppressed in the summary, but we see it in the\n",
@@ -6843,7 +6843,7 @@
   {
    "cell_type": "code",
    "execution_count": 89,
-   "id": "29036a47",
+   "id": "a2d6ddfd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -6854,7 +6854,7 @@
   {
    "cell_type": "code",
    "execution_count": 90,
-   "id": "0a31576a",
+   "id": "1d76f970",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -6896,7 +6896,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4abe857cc251478b8855f53164fa5b45",
+       "model_id": "e24a4171456b412db47d6577cb336c37",
        "version_major": 2,
        "version_minor": 0
       },
@@ -7206,7 +7206,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a20a2113",
+   "id": "1cbf1b42",
    "metadata": {},
    "source": [
     "The rest is now similar to other networks we have fit. We\n",
@@ -7216,13 +7216,13 @@
   {
    "cell_type": "code",
    "execution_count": 91,
-   "id": "b2602a79",
+   "id": "d8a60d35",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1648bfd17bc04050b412c36afb32e092",
+       "model_id": "4e512ef8a7d348eea7f423a9ed96c86e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -7240,8 +7240,8 @@
        "┃<span style=\"font-weight: bold\">   Runningstage.testing    </span>┃<span style=\"font-weight: bold\">                           </span>┃\n",
        "┃<span style=\"font-weight: bold\">          metric           </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">       test_accuracy       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8388800024986267     </span>│\n",
-       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_loss         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8145671486854553     </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\">       test_accuracy       </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8480799794197083     </span>│\n",
+       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_loss         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.7677657604217529     </span>│\n",
        "└───────────────────────────┴───────────────────────────┘\n",
        "</pre>\n"
       ],
@@ -7250,8 +7250,8 @@
        "┃\u001b[1m \u001b[0m\u001b[1m  Runningstage.testing   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m                           \u001b[0m┃\n",
        "┃\u001b[1m \u001b[0m\u001b[1m         metric          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│\u001b[36m \u001b[0m\u001b[36m      test_accuracy      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8388800024986267    \u001b[0m\u001b[35m \u001b[0m│\n",
-       "│\u001b[36m \u001b[0m\u001b[36m        test_loss        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8145671486854553    \u001b[0m\u001b[35m \u001b[0m│\n",
+       "│\u001b[36m \u001b[0m\u001b[36m      test_accuracy      \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8480799794197083    \u001b[0m\u001b[35m \u001b[0m│\n",
+       "│\u001b[36m \u001b[0m\u001b[36m        test_loss        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.7677657604217529    \u001b[0m\u001b[35m \u001b[0m│\n",
        "└───────────────────────────┴───────────────────────────┘\n"
       ]
      },
@@ -7261,7 +7261,7 @@
     {
      "data": {
       "text/plain": [
-       "[{'test_loss': 0.8145671486854553, 'test_accuracy': 0.8388800024986267}]"
+       "[{'test_loss': 0.7677657604217529, 'test_accuracy': 0.8480799794197083}]"
       ]
      },
      "execution_count": 91,
@@ -7275,7 +7275,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54857d49",
+   "id": "deca21cd",
    "metadata": {},
    "source": [
     "We once again show the learning progress, followed by cleanup."
@@ -7284,7 +7284,7 @@
   {
    "cell_type": "code",
    "execution_count": 92,
-   "id": "32c3e3da",
+   "id": "65d7276c",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -7301,7 +7301,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 600x600 with 1 Axes>"
       ]
@@ -7325,7 +7325,7 @@
   {
    "cell_type": "code",
    "execution_count": 93,
-   "id": "be5a979e",
+   "id": "c6f2d6c4",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -7341,7 +7341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "240618c9",
+   "id": "f920c659",
    "metadata": {},
    "source": [
     "### Time Series Prediction\n",
@@ -7353,7 +7353,7 @@
   {
    "cell_type": "code",
    "execution_count": 94,
-   "id": "cf219016",
+   "id": "f3e17682",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7368,7 +7368,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2ebf00d3",
+   "id": "e621c2eb",
    "metadata": {},
    "source": [
     "Next we set up the lagged versions of the data, dropping\n",
@@ -7378,7 +7378,7 @@
   {
    "cell_type": "code",
    "execution_count": 95,
-   "id": "c0d6bb22",
+   "id": "78707eda",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7393,7 +7393,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "53d15ddf",
+   "id": "bb83e654",
    "metadata": {},
    "source": [
     "Finally, we extract the response, training indicator, and drop the current day’s `DJ_return` and\n",
@@ -7403,7 +7403,7 @@
   {
    "cell_type": "code",
    "execution_count": 96,
-   "id": "9de511d5",
+   "id": "4d894824",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -7431,7 +7431,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c61eaa2",
+   "id": "f1889769",
    "metadata": {},
    "source": [
     "We first fit a simple linear model and compute the $R^2$ on the test data using\n",
@@ -7441,7 +7441,7 @@
   {
    "cell_type": "code",
    "execution_count": 97,
-   "id": "89666a56",
+   "id": "4d7f5ce0",
    "metadata": {},
    "outputs": [
     {
@@ -7463,7 +7463,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c3997039",
+   "id": "02c7294f",
    "metadata": {},
    "source": [
     "We refit this model, including the factor variable `day_of_week`.\n",
@@ -7474,7 +7474,7 @@
   {
    "cell_type": "code",
    "execution_count": 98,
-   "id": "23ebf124",
+   "id": "a6b371bb",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -7487,7 +7487,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8df4e475",
+   "id": "92fda2e1",
    "metadata": {},
    "source": [
     " Note that we do not have\n",
@@ -7498,7 +7498,7 @@
   {
    "cell_type": "code",
    "execution_count": 99,
-   "id": "4190a4dc",
+   "id": "a2a8cc85",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -7521,7 +7521,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d98cc235",
+   "id": "68a22c64",
    "metadata": {},
    "source": [
     "This model achieves an $R^2$ of about 46%."
@@ -7529,7 +7529,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "131bfc1c",
+   "id": "1cca42e4",
    "metadata": {},
    "source": [
     "To fit the RNN, we must reshape the data, as it will expect 5 lagged\n",
@@ -7550,7 +7550,7 @@
   {
    "cell_type": "code",
    "execution_count": 100,
-   "id": "a2b41f92",
+   "id": "8ee6e6a3",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -7581,7 +7581,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b301214",
+   "id": "a1f41334",
    "metadata": {},
    "source": [
     "We now reshape the data."
@@ -7590,7 +7590,7 @@
   {
    "cell_type": "code",
    "execution_count": 101,
-   "id": "dde73c9e",
+   "id": "d35ceb54",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -7613,7 +7613,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4fa6cd7a",
+   "id": "11137e1d",
    "metadata": {},
    "source": [
     "By specifying the first size as -1, `numpy.reshape()` deduces its size based on the remaining arguments.\n",
@@ -7628,7 +7628,7 @@
   {
    "cell_type": "code",
    "execution_count": 102,
-   "id": "6f9d6357",
+   "id": "9e3dc6d5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7649,7 +7649,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5d720e7",
+   "id": "1fd566f7",
    "metadata": {},
    "source": [
     "We  fit the model in a similar fashion to previous networks. We\n",
@@ -7659,14 +7659,13 @@
     "early stopping, since then the test performance would be biased.\n",
     "\n",
     "We form the training dataset similar to\n",
-    "our `Hitters` example.\n",
-    " "
+    "our `Hitters` example."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 103,
-   "id": "a21ca47e",
+   "id": "df5e5ab6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7680,7 +7679,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0d627892",
+   "id": "16565d83",
    "metadata": {},
    "source": [
     "Following our usual pattern, we inspect the summary."
@@ -7689,7 +7688,7 @@
   {
    "cell_type": "code",
    "execution_count": 104,
-   "id": "8fa26b87",
+   "id": "d7f49bec",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -7732,7 +7731,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "065db586",
+   "id": "ad57780a",
    "metadata": {},
    "source": [
     "We again put the two datasets into a data module, with a\n",
@@ -7742,7 +7741,7 @@
   {
    "cell_type": "code",
    "execution_count": 105,
-   "id": "9b871361",
+   "id": "ea7ce0f4",
    "metadata": {
     "lines_to_next_cell": 0
    },
@@ -7757,7 +7756,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8e6d61d8",
+   "id": "d0d60c8b",
    "metadata": {},
    "source": [
     "We run some data through our model to be sure the sizes match up correctly."
@@ -7766,7 +7765,7 @@
   {
    "cell_type": "code",
    "execution_count": 106,
-   "id": "b63d1f85",
+   "id": "ccd77738",
    "metadata": {},
    "outputs": [
     {
@@ -7789,7 +7788,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fc69ade",
+   "id": "c9397b7f",
    "metadata": {},
    "source": [
     "We follow our previous example for setting up a trainer for a\n",
@@ -7800,7 +7799,7 @@
   {
    "cell_type": "code",
    "execution_count": 107,
-   "id": "c5f32a4f",
+   "id": "96e04e3f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -7813,7 +7812,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed00be6a",
+   "id": "9d7ef6fc",
    "metadata": {},
    "source": [
     "Fitting the model should by now be familiar.\n",
@@ -7823,7 +7822,7 @@
   {
    "cell_type": "code",
    "execution_count": 108,
-   "id": "6bd98eb9",
+   "id": "fc6ba2ca",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -7865,7 +7864,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f50cb3862a6e4ddcbcb7131a9c3e6c07",
+       "model_id": "cd881313814b4b319883de36a456b21e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -8537,7 +8536,7 @@
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@@ -10745,7 +10744,7 @@
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-   "id": "3c78b138",
+   "id": "fc55d4f9",
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     "We could also fit a model without the `nn.RNN()` layer by just\n",
@@ -10762,7 +10761,7 @@
   {
    "cell_type": "code",
    "execution_count": 109,
-   "id": "5586a609",
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@@ -10777,7 +10776,7 @@
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-   "id": "c1c361b4",
+   "id": "c7fc165f",
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@@ -10786,7 +10785,7 @@
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-   "id": "b72ecb13",
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@@ -10799,7 +10798,7 @@
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-   "id": "32016e95",
+   "id": "310271e1",
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     "We build a `NonLinearARModel()` that takes as input the 20 features and a hidden layer  with 32 units. The remaining steps are familiar."
@@ -10808,7 +10807,7 @@
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@@ -10827,7 +10826,7 @@
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@@ -10841,7 +10840,7 @@
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     "We continue with the usual training steps, fit the model,\n",
@@ -10851,7 +10850,7 @@
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-   "id": "47bdb236",
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@@ -11251,7 +11250,7 @@
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     "              \n",
@@ -11263,8 +11262,8 @@
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-   "formats": "ipynb,Rmd",
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