diff --git a/sentiment-network/Sentiment_Classification_Projects.ipynb b/sentiment-network/Sentiment_Classification_Projects.ipynb
index 16fe59612a..ae891fbfbf 100644
--- a/sentiment-network/Sentiment_Classification_Projects.ipynb
+++ b/sentiment-network/Sentiment_Classification_Projects.ipynb
@@ -1401,7 +1401,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "hist, edges = np.histogram(list(map(lambda x:x[1],pos_neg_ratios.most_common())), density=True, bins=100, normed=True)\n",
+    "hist, edges = np.histogram(list(map(lambda x:x[1],pos_neg_ratios.most_common())), density=True, bins=100)\n",
     "\n",
     "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n",
     "           toolbar_location=\"above\",\n",
@@ -1428,7 +1428,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "hist, edges = np.histogram(list(map(lambda x:x[1],frequency_frequency.most_common())), density=True, bins=100, normed=True)\n",
+    "hist, edges = np.histogram(list(map(lambda x:x[1],frequency_frequency.most_common())), density=True, bins=100)\n",
     "\n",
     "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n",
     "           toolbar_location=\"above\",\n",
@@ -1541,9 +1541,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "source": [
     "# Analysis: What's Going on in the Weights?<a id='lesson_7'></a>"
    ]
@@ -1656,7 +1654,7 @@
    "source": [
     "from sklearn.manifold import TSNE\n",
     "tsne = TSNE(n_components=2, random_state=0)\n",
-    "words_top_ted_tsne = tsne.fit_transform(vectors_list)"
+    "words_top_ted_tsne = tsne.fit_transform(np.array(vectors_list))"
    ]
   },
   {
@@ -1690,7 +1688,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1704,9 +1702,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/sentiment-network/Sentiment_Classification_Solutions.ipynb b/sentiment-network/Sentiment_Classification_Solutions.ipynb
index 60cedc1789..74dfea4cab 100644
--- a/sentiment-network/Sentiment_Classification_Solutions.ipynb
+++ b/sentiment-network/Sentiment_Classification_Solutions.ipynb
@@ -78,6 +78,9 @@
    "execution_count": 1,
    "metadata": {
     "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
     "nbpresent": {
      "id": "eba2b193-0419-431e-8db9-60f34dd3fe83"
     }
@@ -227,7 +230,10 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "outputs": [],
    "source": [
@@ -246,7 +252,10 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "outputs": [],
    "source": [
@@ -269,7 +278,10 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "outputs": [],
    "source": [
@@ -2348,7 +2360,10 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "outputs": [],
    "source": [
@@ -2416,7 +2431,10 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "outputs": [],
    "source": [
@@ -3618,9 +3636,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "source": [
     "# Project 2: Creating the Input/Output Data<a id='project_2'></a>\n",
     "\n",
@@ -3631,7 +3647,10 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "outputs": [],
    "source": [
@@ -3703,7 +3722,10 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "outputs": [],
    "source": [
@@ -4806,7 +4828,7 @@
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -4870,7 +4892,7 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -4998,9 +5020,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "source": [
     "**TODO:** We've included the framework of a class called `SentimentNetork`. Implement all of the items marked `TODO` in the code. These include doing the following:\n",
     "- Create a basic neural network much like the networks you've seen in earlier lessons and in Project 1, with an input layer, a hidden layer, and an output layer. \n",
@@ -5023,7 +5043,7 @@
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -5265,7 +5285,7 @@
    "cell_type": "code",
    "execution_count": 33,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -5450,7 +5470,7 @@
    "cell_type": "code",
    "execution_count": 39,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -5490,7 +5510,7 @@
    "cell_type": "code",
    "execution_count": 41,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -5501,7 +5521,7 @@
    "cell_type": "code",
    "execution_count": 42,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -5645,7 +5665,7 @@
    "cell_type": "code",
    "execution_count": 44,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -5966,7 +5986,7 @@
    "cell_type": "code",
    "execution_count": 48,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -5997,7 +6017,7 @@
    "cell_type": "code",
    "execution_count": 50,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -6029,7 +6049,7 @@
    "cell_type": "code",
    "execution_count": 52,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -6060,7 +6080,7 @@
    "cell_type": "code",
    "execution_count": 54,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -6071,7 +6091,7 @@
    "cell_type": "code",
    "execution_count": 55,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -6082,7 +6102,7 @@
    "cell_type": "code",
    "execution_count": 56,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -6135,7 +6155,7 @@
    "cell_type": "code",
    "execution_count": 59,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -6146,7 +6166,7 @@
    "cell_type": "code",
    "execution_count": 60,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -6209,7 +6229,7 @@
    "cell_type": "code",
    "execution_count": 62,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -6507,1006 +6527,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
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      ]
     }
@@ -8940,7 +7960,7 @@
     }
    ],
    "source": [
-    "hist, edges = np.histogram(list(map(lambda x:x[1],pos_neg_ratios.most_common())), density=True, bins=100, normed=True)\n",
+    "hist, edges = np.histogram(list(map(lambda x:x[1],pos_neg_ratios.most_common())), density=True, bins=100)\n",
     "\n",
     "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n",
     "           toolbar_location=\"above\",\n",
@@ -8953,7 +7973,7 @@
    "cell_type": "code",
    "execution_count": 70,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -9119,7 +8139,7 @@
     }
    ],
    "source": [
-    "hist, edges = np.histogram(list(map(lambda x:x[1],frequency_frequency.most_common())), density=True, bins=100, normed=True)\n",
+    "hist, edges = np.histogram(list(map(lambda x:x[1],frequency_frequency.most_common())), density=True, bins=100)\n",
     "\n",
     "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n",
     "           toolbar_location=\"above\",\n",
@@ -9157,7 +8177,7 @@
    "cell_type": "code",
    "execution_count": 72,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -9504,1006 +8524,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
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     }
@@ -11584,7 +8604,10 @@
    "cell_type": "code",
    "execution_count": 77,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "outputs": [],
    "source": [
@@ -11643,7 +8666,7 @@
    "cell_type": "code",
    "execution_count": 80,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -13700,7 +10723,7 @@
    "cell_type": "code",
    "execution_count": 83,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -13720,7 +10743,7 @@
    "cell_type": "code",
    "execution_count": 84,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -13745,13 +10768,13 @@
    "cell_type": "code",
    "execution_count": 85,
    "metadata": {
-    "collapsed": true
+    "tags": []
    },
    "outputs": [],
    "source": [
     "from sklearn.manifold import TSNE\n",
     "tsne = TSNE(n_components=2, random_state=0)\n",
-    "words_top_ted_tsne = tsne.fit_transform(vectors_list)"
+    "words_top_ted_tsne = tsne.fit_transform(np.array(vectors_list))"
    ]
   },
   {
@@ -13953,7 +10976,10 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
    },
    "outputs": [],
    "source": []
@@ -13962,7 +10988,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -13976,9 +11002,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }