Update libraries to latest version, including TensorFlow 2.4.1 and Scikit-Learn 0.24.1

03_classification.ipynb

@@ -345,7 +345,7 @@
     "* first, Scikit-Learn and other libraries evolve, and algorithms get tweaked a bit, which may change the exact result you get. If you use the latest Scikit-Learn version (and in general, you really should), you probably won't be using the exact same version I used when I wrote the book or this notebook, hence the difference. I try to keep this notebook reasonably up to date, but I can't change the numbers on the pages in your copy of the book.\n",
     "* second, many training algorithms are stochastic, meaning they rely on randomness. In principle, it's possible to get consistent outputs from a random number generator by setting the seed from which it generates the pseudo-random numbers (which is why you will see `random_state=42` or `np.random.seed(42)` pretty often). However, sometimes this does not suffice due to the other factors listed here.\n",
     "* third, if the training algorithm runs across multiple threads (as do some algorithms implemented in C) or across multiple processes (e.g., when using the `n_jobs` argument), then the precise order in which operations will run is not always guaranteed, and thus the exact result may vary slightly.\n",
-    "* lastly, other things may prevent perfect reproducibility, such as Python maps and sets whose order is not guaranteed to be stable across sessions, or the order of files in a directory which is also not guaranteed."
+    "* lastly, other things may prevent perfect reproducibility, such as Python dicts and sets whose order is not guaranteed to be stable across sessions, or the order of files in a directory which is also not guaranteed."
    ]
   },
   {
@@ -393,7 +393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -412,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -481,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -491,7 +491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -502,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -533,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -542,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -564,7 +564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -573,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -582,7 +582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -591,7 +591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -600,7 +600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -616,7 +616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -627,7 +627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -651,7 +651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -669,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -681,7 +681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -691,7 +691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -713,7 +713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -722,7 +722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -732,7 +732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -836,6 +836,13 @@
     "sgd_clf.decision_function([some_digit])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Warning**: the following two cells may take close to 30 minutes to run, or more depending on your hardware."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 62,
@@ -1202,7 +1209,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Warning**: the next cell may take hours to run, depending on your hardware."
+    "**Warning**: the next cell may take close to 16 hours to run, or more depending on your hardware."
    ]
   },
   {
@@ -1348,6 +1355,13 @@
     "knn_clf.fit(X_train_augmented, y_train_augmented)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Warning**: the following cell may take close to an hour to run, depending on your hardware."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 99,
@@ -1918,7 +1932,7 @@
    "source": [
     "import os\n",
     "import tarfile\n",
-    "import urllib\n",
+    "import urllib.request\n",
     "\n",
     "DOWNLOAD_ROOT = \"http://spamassassin.apache.org/old/publiccorpus/\"\n",
     "HAM_URL = DOWNLOAD_ROOT + \"20030228_easy_ham.tar.bz2\"\n",
@@ -2149,7 +2163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2510,7 +2524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 158,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2533,7 +2547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 159,
    "metadata": {},
    "outputs": [],
    "source": [