Post-session upload: AmirAbdi/18...

UBC-CS · Mar 27, 2023 · afe0990 · afe0990
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diff --git a/.gitignore b/.gitignore
@@ -13,3 +13,4 @@ lectures/data/housing-kaggle
 *.pyc
 *.tsv
 *.wav
+lectures/data/animal-faces/
diff --git a/lectures/AmirAbdi/17_natural-language-processing.ipynb b/lectures/AmirAbdi/17_natural-language-processing.ipynb
@@ -147,7 +147,7 @@
     "- Describe the reasons and benefits of using pre-trained embeddings. \n",
     "- Load and use pre-trained word embeddings to find word similarities and analogies. \n",
     "- Demonstrate biases in embeddings and learn to watch out for such biases in pre-trained embeddings.\n",
-    "- Use word embeddings in text classification and document clustering using `spaCy`.\n",
+    "- Use word **embeddings** in text classification and document clustering using `spaCy`.\n",
     "- Explain the general idea of topic modeling. \n",
     "- Describe the input and output of topic modeling. \n",
     "- Carry out basic text preprocessing using `spaCy`.   "
@@ -577,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 111,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -610,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
@@ -628,7 +628,7 @@
        "True"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -647,7 +647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -656,7 +656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 114,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -732,7 +732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 115,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1109,7 +1109,7 @@
        "[6342 rows x 6342 columns]"
       ]
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-     "execution_count": 6,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1129,7 +1129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1163,7 +1163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
@@ -2031,7 +2031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 121,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2139,7 +2139,7 @@
        "9               Habs  0.661023"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8106,6 +8106,84 @@
     "#### Visualize topics"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lda.per_word_topics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(3319, 0.0022999633),\n",
+       " (3336, 0.0013174261),\n",
+       " (658, 0.0012206235),\n",
+       " (3424, 0.0012169023),\n",
+       " (1118, 0.0011576178),\n",
+       " (3788, 0.0011387375),\n",
+       " (1117, 0.0011356168),\n",
+       " (1821, 0.0008851419),\n",
+       " (3612, 0.00069421926),\n",
+       " (3499, 0.0006651815)]"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lda.get_topic_terms(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(114, 0.008096032),\n",
+       " (790, 0.008090814),\n",
+       " (865, 0.0070692785),\n",
+       " (1667, 0.0067776213),\n",
+       " (900, 0.006763307),\n",
+       " (863, 0.0062548574),\n",
+       " (1161, 0.005847539),\n",
+       " (857, 0.0057668686),\n",
+       " (1136, 0.005758805),\n",
+       " (164, 0.005744651)]"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lda.get_topic_terms(1)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 96,

diff --git a/lectures/AmirAbdi/18_intro_to_computer-vision.ipynb b/lectures/AmirAbdi/18_intro_to_computer-vision.ipynb
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -84,23 +84,6 @@
     "from sklearn.preprocessing import StandardScaler"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Learning objectives\n",
-    "\n",
-    "- Apply classifiers to **multi-class classification** algorithms.\n",
-    "- Explain the role of neural networks in machine learning, and the pros/cons of using them.\n",
-    "- Explain why the methods we've learned previously would not be effective on **image data**.\n",
-    "- Apply **pre-trained neural networks** to classification and regression problems.\n",
-    "- Utilize pre-trained networks as feature extractors and combine them with models we've learned previously."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -136,7 +119,12 @@
    "metadata": {},
    "source": [
     "Answers:\n",
-    " -"
+    " - A\n",
+    " - B\n",
+    " - C\n",
+    " - D\n",
+    " \n",
+    " E is incorrect becasue we don't get the \"labels\" out of LDA; we just get K topics, and don't know their labels until we study them closely ourselves."
    ]
   },
   {
@@ -146,6 +134,23 @@
     "<br><br>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Learning objectives\n",
+    "\n",
+    "- Apply classifiers to **multi-class classification** algorithms.\n",
+    "- Explain the role of neural networks in machine learning, and the pros/cons of using them.\n",
+    "- Explain why the methods we've learned previously would not be effective on **image data**.\n",
+    "- Apply **pre-trained neural networks** to classification and regression problems.\n",
+    "- Utilize pre-trained networks as feature extractors and combine them with models we've learned previously."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -188,7 +193,7 @@
     }
    },
    "source": [
-    "### One vs. Rest \n",
+    "### One vs. Rest (OVR)\n",
     "\n",
     "- 1v{2,3}, 2v{1,3}, 3v{1,2}\n",
     "- Learn a binary model for each class which tries to separate that class from all of the other classes.\n",
@@ -706,6 +711,14 @@
     "- (B) For a 100-class classification problem, one-vs.-rest multi-class strategy will create 100 binary classifiers. "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answers:\n",
+    "- B"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2111,7 +2124,7 @@
     "        # Zero the parameter gradients\n",
     "        optimizer.zero_grad()\n",
     "        \n",
-    "        # Forward + backward + optimize\n",
+    "        # Feed Forward + backward (back propagation) + optimize (updating weights)\n",
     "        outputs = net(inputs)\n",
     "        loss = criterion(outputs, labels)\n",
     "        loss.backward()\n",
@@ -2207,6 +2220,7 @@
     "In the last lecture we used pre-trained embeddings to create text representations.     \n",
     "We didn't train any models.  \n",
     "**Q: Was that Transfer Learning?**\n",
+    "- Yes\n",
     "\n",
     "<br><br><br><br>"
    ]
@@ -2499,7 +2513,7 @@
     "\n",
     "**Q: Can this model classify images beyond the classes that it was trained on?**\n",
     "\n",
-    "A: ???"
+    "A: Yes, with transfer learning"
    ]
   },
   {
@@ -2753,7 +2767,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- Now for each image in our dataset, we'll **extract a feature vector from a pre-trained model called densenet121**, which is trained on the ImageNet dataset.  "
+    "- Now for each image in our dataset, we'll **extract a feature vector from a pre-trained model called densenet121**, which is trained on the **ImageNet dataset**.  "
    ]
   },
   {
-Original file line number
+Diff line change
@@ Expand Up / @@ -13,3 +13,4 @@ lectures/data/housing-kaggle @@
     *.pyc
     *.tsv
     *.wav
+    lectures/data/animal-faces/