From d9e049e9646a29015486b11c7d8d1612e92bb919 Mon Sep 17 00:00:00 2001
From: johnwhitegm <johnwhitegm@gmail.com>
Date: Mon, 4 May 2026 20:28:40 -0400
Subject: [PATCH 01/11] Created using Colab

---
 building-a-brain/BuildingABrain.ipynb | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/building-a-brain/BuildingABrain.ipynb b/building-a-brain/BuildingABrain.ipynb
index 2af0b0c..ab04a23 100644
--- a/building-a-brain/BuildingABrain.ipynb
+++ b/building-a-brain/BuildingABrain.ipynb
@@ -4,8 +4,7 @@
   "metadata": {
     "colab": {
       "name": "BuildingABrain.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
+      "provenance": []
     },
     "kernelspec": {
       "name": "python3",
@@ -36,7 +35,7 @@
       "source": [
         "# Building a Brain in 10 Minutes\n",
         "\n",
-        "Many decades ago, artificial neural networks were developed to mimic the learning capabilities of humans and animals. Below is an excerpt from [The Machine that Changed the World](https://www.youtube.com/watch?v=enWWlx7-t0k&t=166s), a 1992 documentary about Artificial Intelligence."
+        "Many decades ago, artificial neural networks were developed to mimic the learning capabilities of humans and animals. Below is an excerpt from [The Machine that Changed the World](https://www.youtube.com/watch?v=enWWlx7-t0k&t=166s), a 1992 documentary about Artificial Intelligence. Test"
       ]
     },
     {
@@ -566,4 +565,4 @@
       ]
     }
   ]
-}
+}
\ No newline at end of file

From 78e28e7ddb3d3550e58e3e3b92582cd0bb64084a Mon Sep 17 00:00:00 2001
From: johnwhitegm <johnwhitegm@gmail.com>
Date: Mon, 4 May 2026 20:55:59 -0400
Subject: [PATCH 02/11] Created using Colab

---
 building-a-brain/BuildingABrain.ipynb | 686 ++++++++++++++++++++++++--
 1 file changed, 632 insertions(+), 54 deletions(-)

diff --git a/building-a-brain/BuildingABrain.ipynb b/building-a-brain/BuildingABrain.ipynb
index ab04a23..1ec2eae 100644
--- a/building-a-brain/BuildingABrain.ipynb
+++ b/building-a-brain/BuildingABrain.ipynb
@@ -35,20 +35,51 @@
       "source": [
         "# Building a Brain in 10 Minutes\n",
         "\n",
-        "Many decades ago, artificial neural networks were developed to mimic the learning capabilities of humans and animals. Below is an excerpt from [The Machine that Changed the World](https://www.youtube.com/watch?v=enWWlx7-t0k&t=166s), a 1992 documentary about Artificial Intelligence. Test"
+        "Many decades ago, artificial neural networks were developed to mimic the learning capabilities of humans and animals. Below is an excerpt from [The Machine that Changed the World](https://www.youtube.com/watch?v=enWWlx7-t0k&t=166s), a 1992 documentary about Artificial Intelligence."
       ]
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "Jj9625DV7oKS"
+        "id": "Jj9625DV7oKS",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 336
+        },
+        "outputId": "385cfabb-dbbd-43c0-ca60-39b705fb3ff2"
       },
       "source": [
-        "from IPython.display import YouTubeVideo\n",
-        "YouTubeVideo('cNxadbrN_aI')"
+        "from IPython.display import HTML\n",
+        "\n",
+        "HTML(\"\"\"\n",
+        "<iframe width=\"560\" height=\"315\"\n",
+        "src=\"https://www.youtube.com/embed/cNxadbrN_aI\"\n",
+        "frameborder=\"0\"\n",
+        "allowfullscreen\n",
+        "referrerpolicy=\"strict-origin-when-cross-origin\"></iframe>\n",
+        "\"\"\")"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "<iframe width=\"560\" height=\"315\"\n",
+              "src=\"https://www.youtube.com/embed/cNxadbrN_aI\"\n",
+              "frameborder=\"0\"\n",
+              "allowfullscreen\n",
+              "referrerpolicy=\"strict-origin-when-cross-origin\"></iframe>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -64,15 +95,30 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "boTcxf3jUGum"
+        "id": "boTcxf3jUGum",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d119a6a7-7841-40db-ae7f-895e4d7398cf"
       },
       "source": [
         "import tensorflow as tf\n",
         "\n",
         "tf.config.list_physical_devices('GPU')"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -98,14 +144,33 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "ijLiERH_n6q_"
+        "id": "ijLiERH_n6q_",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0b47c992-29f7-4541-c61c-f233bd88ae86"
       },
       "source": [
         "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
         "(train_images, train_labels), (valid_images, valid_labels) = fashion_mnist.load_data()"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
+            "\u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
+            "\u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
+            "\u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
+            "\u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -121,7 +186,12 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "sogb0Tt1xfni"
+        "id": "sogb0Tt1xfni",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 430
+        },
+        "outputId": "828cee7e-54f1-4c88-ee95-110a6e01b56e"
       },
       "source": [
         "import matplotlib.pyplot as plt\n",
@@ -135,8 +205,19 @@
         "plt.grid(False)\n",
         "plt.show()"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -165,13 +246,28 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "CQdadtT11iI8"
+        "id": "CQdadtT11iI8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "398a0772-8357-4406-830f-e51d89eca2cc"
       },
       "source": [
         "train_labels[data_idx]"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.uint8(9)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -187,7 +283,12 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "ZNrxxfNk3it1"
+        "id": "ZNrxxfNk3it1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 430
+        },
+        "outputId": "3d41b1e3-50b7-4373-e589-f6dcffa4eea9"
       },
       "source": [
         "import matplotlib.pyplot as plt\n",
@@ -201,19 +302,45 @@
         "plt.grid(False)\n",
         "plt.show()"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "AeJ68DMn4f3g"
+        "id": "AeJ68DMn4f3g",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0d137043-801f-48d3-c830-a5e913219322"
       },
       "source": [
         "valid_labels[data_idx]"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.uint8(7)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -232,7 +359,7 @@
         "\n",
         "## Defining the architecture\n",
         "<center>\n",
-        "<a title=\"BruceBlaus, CC BY 3.0 &lt;https://creativecommons.org/licenses/by/3.0&gt;, via Wikimedia Commons\" href=\"https://commons.wikimedia.org/wiki/File:Blausen_0657_MultipolarNeuron.png\"><img width=\"512\" alt=\"Blausen 0657 MultipolarNeuron\" src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/1/10/Blausen_0657_MultipolarNeuron.png/512px-Blausen_0657_MultipolarNeuron.png\"></a>\n",
+        "<a title=\"BruceBlaus, CC BY 3.0 &lt;https://creativecommons.org/licenses/by/3.0&gt;, via Wikimedia Commons\" href=\"https://commons.wikimedia.org/wiki/File:Blausen_0657_MultipolarNeuron.png\"><img width=\"512\" alt=\"Blausen 0657 MultipolarNeuron\" src=\"https://upload.wikimedia.org/wikipedia/commons/1/10/Blausen_0657_MultipolarNeuron.png?utm_source=commons.wikimedia.org&utm_campaign=index&utm_content=original\"></a>\n",
         "<p><small>\n",
         "Image courtesy of <a href=\"https://commons.wikimedia.org/wiki/File:Blausen_0657_MultipolarNeuron.png\">Wikimedia Commons</a>\n",
         "</small></p>\n",
@@ -255,14 +382,222 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "dHq93yV8_FWR"
+        "id": "dHq93yV8_FWR",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 68
+        },
+        "outputId": "dd8a1fcb-beac-4848-dcf2-cd8f282c4169"
       },
       "source": [
         "# 28 lists with 28 values each\n",
         "valid_images[data_idx]"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
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+              "          0,   0]], dtype=uint8)"
+            ],
+            "text/html": [
+              "<style>\n",
+              "      .ndarray_repr .ndarray_raw_data {\n",
+              "        display: none;\n",
+              "      }\n",
+              "      .ndarray_repr.show_array .ndarray_raw_data {\n",
+              "        display: block;\n",
+              "      }\n",
+              "      .ndarray_repr.show_array .ndarray_image_preview {\n",
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+              "      </style>\n",
+              "      <div id=\"id-756e8e70-8f2b-4994-bc51-1a7ad20c9bc2\" class=\"ndarray_repr\"><pre>ndarray (28, 28) <button style=\"padding: 0 2px;\">show data</button></pre><img src=\"data:image/png;base64,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\" class=\"ndarray_image_preview\" /><pre class=\"ndarray_raw_data\">array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
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+              "          0,   0],\n",
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+              "          0,   0],\n",
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+              "          0,   0],\n",
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+              "          0,   0],\n",
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+              "        138, 174, 186, 162, 169, 177, 200, 214, 206, 178, 179, 205, 227,\n",
+              "          6,   0],\n",
+              "       [  3, 104, 128, 126, 124, 105,  85,  75,  99, 181, 140, 141, 185,\n",
+              "        179, 181, 183, 185, 204, 227, 255, 224, 226, 234, 227, 159, 198,\n",
+              "        198,   0],\n",
+              "       [ 62, 150, 115, 111,  91, 117, 131, 102, 129, 120, 153, 176, 216,\n",
+              "        232, 238, 238, 211, 242, 173,  26,   0,  54, 104,  20,   0,   0,\n",
+              "         48,  16],\n",
+              "       [116, 179, 173, 173, 202, 188, 200, 228, 179, 150, 100,  89, 164,\n",
+              "         39,   0,  82, 129,  17,   0,   0,   0,   0,   0,   0,  25,  11,\n",
+              "          0,  12],\n",
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+              "          0,   0,   0,   0,   0,   9,  14,   7,  18,   9,   5,   0,  12,\n",
+              "          0,   0],\n",
+              "       [  0,   5,  57,  67,   0,   0,   0,   1,   0,   0,   8,   8,  71,\n",
+              "         55,  11,  32,  30,  30,  17,   4,   3,   9,  15,  17,  29,  37,\n",
+              "         92, 106],\n",
+              "       [  0,   0,   0,  58,  50, 142, 169,  80, 144, 156, 148, 159, 152,\n",
+              "        156, 136, 151, 142,  70, 139, 161,  98, 131, 145,  60, 141, 121,\n",
+              "         77,   0],\n",
+              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
+              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
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+              "          0,   0],\n",
+              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
+              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
+              "          0,   0],\n",
+              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
+              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
+              "          0,   0],\n",
+              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
+              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
+              "          0,   0],\n",
+              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
+              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
+              "          0,   0]], dtype=uint8)</pre></div><script>\n",
+              "      (() => {\n",
+              "      const titles = ['show data', 'hide data'];\n",
+              "      let index = 0\n",
+              "      document.querySelector('#id-756e8e70-8f2b-4994-bc51-1a7ad20c9bc2 button').onclick = (e) => {\n",
+              "        document.querySelector('#id-756e8e70-8f2b-4994-bc51-1a7ad20c9bc2').classList.toggle('show_array');\n",
+              "        index = (++index) % 2;\n",
+              "        document.querySelector('#id-756e8e70-8f2b-4994-bc51-1a7ad20c9bc2 button').textContent = titles[index];\n",
+              "        e.preventDefault();\n",
+              "        e.stopPropagation();\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -286,14 +621,29 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "SIgbDeIbqELs"
+        "id": "SIgbDeIbqELs",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9c1efbcc-b259-4093-c675-26779293ce39"
       },
       "source": [
         "number_of_classes = train_labels.max() + 1\n",
         "number_of_classes"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.uint8(10)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
     },
     {
       "cell_type": "code",
@@ -302,11 +652,12 @@
       },
       "source": [
         "model = tf.keras.Sequential([\n",
-        "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
-        "    tf.keras.layers.Dense(number_of_classes)\n",
+        "    tf.keras.Input(shape=(28, 28)), # Use Input layer to define shape\n",
+        "    tf.keras.layers.Flatten(),\n",
+        "    tf.keras.layers.Dense(int(number_of_classes))\n",
         "])"
       ],
-      "execution_count": null,
+      "execution_count": 30,
       "outputs": []
     },
     {
@@ -323,13 +674,96 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "wYSHZNPEA1tb"
+        "id": "wYSHZNPEA1tb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 193
+        },
+        "outputId": "85d482b0-286a-413a-f9a8-99d19325ca49"
       },
       "source": [
         "model.summary()"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ flatten_5 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m7,850\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ flatten_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">784</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m7,850\u001b[0m (30.66 KB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> (30.66 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m7,850\u001b[0m (30.66 KB)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> (30.66 KB)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -343,17 +777,32 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "00aNzMfrQARw"
+        "id": "00aNzMfrQARw",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8e40f331-7b39-4001-fbb4-3dfaf7b592ea"
       },
       "source": [
         "image_height = 28\n",
         "image_width = 28\n",
         "\n",
-        "number_of_weights = image_height * image_width * number_of_classes\n",
+        "number_of_weights = image_height * image_width * int(number_of_classes)\n",
         "number_of_weights"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "7840"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -369,13 +818,30 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "3wY9VLOKKyKw"
+        "id": "3wY9VLOKKyKw",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 498
+        },
+        "outputId": "0f8eeba3-c825-4122-e7a4-501f3873ba70"
       },
       "source": [
         "tf.keras.utils.plot_model(model, show_shapes=True)"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -428,7 +894,7 @@
         "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
         "              metrics=['accuracy'])"
       ],
-      "execution_count": null,
+      "execution_count": 36,
       "outputs": []
     },
     {
@@ -449,7 +915,11 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "A0adp_qwA4Kg"
+        "id": "A0adp_qwA4Kg",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "999eb875-80ea-44e0-900e-64ecd255c053"
       },
       "source": [
         "history = model.fit(\n",
@@ -460,8 +930,25 @@
         "    validation_data=(valid_images, valid_labels)\n",
         ")"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/5\n",
+            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.8044 - loss: 9.6828 - val_accuracy: 0.7917 - val_loss: 11.1440\n",
+            "Epoch 2/5\n",
+            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8054 - loss: 10.1757 - val_accuracy: 0.7772 - val_loss: 13.2141\n",
+            "Epoch 3/5\n",
+            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.8060 - loss: 9.9181 - val_accuracy: 0.7809 - val_loss: 11.4187\n",
+            "Epoch 4/5\n",
+            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8095 - loss: 9.4431 - val_accuracy: 0.7621 - val_loss: 17.4376\n",
+            "Epoch 5/5\n",
+            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.8048 - loss: 10.4435 - val_accuracy: 0.7898 - val_loss: 12.2043\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -485,13 +972,64 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "XcDRhkVWD168"
+        "id": "XcDRhkVWD168",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7896cac5-dcef-4248-b2a3-7fbcbe0c41ea"
       },
       "source": [
         "model.predict(train_images[0:10])"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ -406.26605 ,  -810.9323  ,  -245.22408 ,  -408.96628 ,\n",
+              "         -433.6929  ,   361.1237  ,  -176.61609 ,   338.9754  ,\n",
+              "           58.718292,   530.6108  ],\n",
+              "       [  218.37871 ,  -388.85202 ,    90.183464,   -24.636917,\n",
+              "         -214.07404 , -1627.0286  ,    35.187714, -2225.3076  ,\n",
+              "         -226.93773 , -1239.6675  ],\n",
+              "       [   70.7416  ,   -23.040031,    33.486485,    66.72533 ,\n",
+              "           13.267202,  -557.6654  ,    10.02778 ,  -575.08093 ,\n",
+              "          -99.35069 ,  -336.0345  ],\n",
+              "       [  162.024   ,    82.8766  ,   153.49939 ,   181.7207  ,\n",
+              "           67.74914 ,  -834.94104 ,   130.10194 , -1216.4783  ,\n",
+              "         -144.43387 ,  -827.5984  ],\n",
+              "       [  159.403   ,    68.476814,    76.09903 ,   171.12115 ,\n",
+              "           70.943825, -1045.7615  ,    36.534264,  -810.8569  ,\n",
+              "          -60.227158, -1148.3757  ],\n",
+              "       [   17.732721,  -173.01819 ,   262.7382  ,   -72.45895 ,\n",
+              "          108.93694 , -1121.0135  ,    41.596664, -1926.2325  ,\n",
+              "         -146.07317 , -1275.9569  ],\n",
+              "       [ -217.902   ,  -277.93948 ,  -154.54495 ,  -177.51439 ,\n",
+              "         -230.92055 ,   103.68795 ,  -197.71262 ,   253.50223 ,\n",
+              "           22.665834,    56.596657],\n",
+              "       [ -104.23269 ,  -303.87686 ,   225.72911 ,  -369.8115  ,\n",
+              "          234.82683 , -1584.358   ,   -59.033424, -2176.585   ,\n",
+              "         -134.48302 , -1218.0953  ],\n",
+              "       [  -58.612274,  -566.5974  ,  -137.47835 ,  -220.60683 ,\n",
+              "         -298.25684 ,   474.15652 ,   -93.56001 ,    33.72026 ,\n",
+              "         -102.94882 ,   202.78635 ],\n",
+              "       [  -78.46059 ,  -498.6948  ,    46.64348 ,  -197.15094 ,\n",
+              "         -112.02182 ,   868.1138  ,   -17.570368,   -21.103134,\n",
+              "          114.397484,   200.15775 ]], dtype=float32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 39
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -522,7 +1060,12 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "7wiFSYdcM4Nt"
+        "id": "7wiFSYdcM4Nt",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 882
+        },
+        "outputId": "bd693768-6c60-44b3-f20c-1b528719778b"
       },
       "source": [
         "data_idx = 8675 # The question number to study with. Feel free to change up to 59999.\n",
@@ -541,8 +1084,43 @@
         "\n",
         "print(\"correct answer:\", train_labels[data_idx])"
       ],
-      "execution_count": null,
-      "outputs": []
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "correct answer: 6\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "markdown",

From 37b3c416d8bc9febeb582a035cf3fb256702e315 Mon Sep 17 00:00:00 2001
From: johnwhitegm <johnwhitegm@gmail.com>
Date: Mon, 4 May 2026 20:58:19 -0400
Subject: [PATCH 03/11] Created using Colab

---
 building-a-brain/BuildingABrain.ipynb | 664 ++------------------------
 1 file changed, 47 insertions(+), 617 deletions(-)

diff --git a/building-a-brain/BuildingABrain.ipynb b/building-a-brain/BuildingABrain.ipynb
index 1ec2eae..d10ec62 100644
--- a/building-a-brain/BuildingABrain.ipynb
+++ b/building-a-brain/BuildingABrain.ipynb
@@ -41,12 +41,7 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "Jj9625DV7oKS",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 336
-        },
-        "outputId": "385cfabb-dbbd-43c0-ca60-39b705fb3ff2"
+        "id": "Jj9625DV7oKS"
       },
       "source": [
         "from IPython.display import HTML\n",
@@ -59,27 +54,8 @@
         "referrerpolicy=\"strict-origin-when-cross-origin\"></iframe>\n",
         "\"\"\")"
       ],
-      "execution_count": 16,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "<iframe width=\"560\" height=\"315\"\n",
-              "src=\"https://www.youtube.com/embed/cNxadbrN_aI\"\n",
-              "frameborder=\"0\"\n",
-              "allowfullscreen\n",
-              "referrerpolicy=\"strict-origin-when-cross-origin\"></iframe>\n"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 16
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -95,30 +71,15 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "boTcxf3jUGum",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "d119a6a7-7841-40db-ae7f-895e4d7398cf"
+        "id": "boTcxf3jUGum"
       },
       "source": [
         "import tensorflow as tf\n",
         "\n",
         "tf.config.list_physical_devices('GPU')"
       ],
-      "execution_count": 17,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 17
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -144,33 +105,14 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "ijLiERH_n6q_",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "0b47c992-29f7-4541-c61c-f233bd88ae86"
+        "id": "ijLiERH_n6q_"
       },
       "source": [
         "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
         "(train_images, train_labels), (valid_images, valid_labels) = fashion_mnist.load_data()"
       ],
-      "execution_count": 18,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
-            "\u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
-            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
-            "\u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
-            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
-            "\u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
-            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
-            "\u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
-          ]
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -186,12 +128,7 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "sogb0Tt1xfni",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 430
-        },
-        "outputId": "828cee7e-54f1-4c88-ee95-110a6e01b56e"
+        "id": "sogb0Tt1xfni"
       },
       "source": [
         "import matplotlib.pyplot as plt\n",
@@ -205,19 +142,8 @@
         "plt.grid(False)\n",
         "plt.show()"
       ],
-      "execution_count": 19,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -246,28 +172,13 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "CQdadtT11iI8",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "398a0772-8357-4406-830f-e51d89eca2cc"
+        "id": "CQdadtT11iI8"
       },
       "source": [
         "train_labels[data_idx]"
       ],
-      "execution_count": 20,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "np.uint8(9)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 20
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -283,12 +194,7 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "ZNrxxfNk3it1",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 430
-        },
-        "outputId": "3d41b1e3-50b7-4373-e589-f6dcffa4eea9"
+        "id": "ZNrxxfNk3it1"
       },
       "source": [
         "import matplotlib.pyplot as plt\n",
@@ -302,45 +208,19 @@
         "plt.grid(False)\n",
         "plt.show()"
       ],
-      "execution_count": 21,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "code",
       "metadata": {
-        "id": "AeJ68DMn4f3g",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "0d137043-801f-48d3-c830-a5e913219322"
+        "id": "AeJ68DMn4f3g"
       },
       "source": [
         "valid_labels[data_idx]"
       ],
-      "execution_count": 22,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "np.uint8(7)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 22
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -382,222 +262,14 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "dHq93yV8_FWR",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 68
-        },
-        "outputId": "dd8a1fcb-beac-4848-dcf2-cd8f282c4169"
+        "id": "dHq93yV8_FWR"
       },
       "source": [
         "# 28 lists with 28 values each\n",
         "valid_images[data_idx]"
       ],
-      "execution_count": 23,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
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-              "        138, 174, 186, 162, 169, 177, 200, 214, 206, 178, 179, 205, 227,\n",
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-              "        198,   0],\n",
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-              "        232, 238, 238, 211, 242, 173,  26,   0,  54, 104,  20,   0,   0,\n",
-              "         48,  16],\n",
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-              "         39,   0,  82, 129,  17,   0,   0,   0,   0,   0,   0,  25,  11,\n",
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-              "          0,   0,   0,   0,   0,   9,  14,   7,  18,   9,   5,   0,  12,\n",
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-              "         55,  11,  32,  30,  30,  17,   4,   3,   9,  15,  17,  29,  37,\n",
-              "         92, 106],\n",
-              "       [  0,   0,   0,  58,  50, 142, 169,  80, 144, 156, 148, 159, 152,\n",
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-            ],
-            "text/html": [
-              "<style>\n",
-              "      .ndarray_repr .ndarray_raw_data {\n",
-              "        display: none;\n",
-              "      }\n",
-              "      .ndarray_repr.show_array .ndarray_raw_data {\n",
-              "        display: block;\n",
-              "      }\n",
-              "      .ndarray_repr.show_array .ndarray_image_preview {\n",
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-              "      </style>\n",
-              "      <div id=\"id-756e8e70-8f2b-4994-bc51-1a7ad20c9bc2\" class=\"ndarray_repr\"><pre>ndarray (28, 28) <button style=\"padding: 0 2px;\">show data</button></pre><img src=\"data:image/png;base64,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\" class=\"ndarray_image_preview\" /><pre class=\"ndarray_raw_data\">array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
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-              "        144, 248, 155, 105, 174,  87,   0,   0,   0, 233, 253, 213,   8,\n",
-              "          0,   1],\n",
-              "       [  1,   0,   0,   0,   1,   2,   2,   0,   0,   0,   0,  99, 233,\n",
-              "        119,  81,  89, 140, 183, 208, 139,  74, 218, 223, 184, 189, 145,\n",
-              "          0,   0],\n",
-              "       [  0,   1,   2,   3,   5,   0,   0,   0,   0, 173, 153, 195,  97,\n",
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-              "          0,   0],\n",
-              "       [  1,   0,   0,   0,   0,  10, 129, 202, 198, 129, 149,  84, 124,\n",
-              "        236, 221, 209, 226, 221, 166, 186, 179, 178, 205, 177, 206, 196,\n",
-              "          0,   0],\n",
-              "       [  0,   0,   6,  49,  80, 148, 203, 181, 203, 164, 145, 178, 159,\n",
-              "        138, 174, 186, 162, 169, 177, 200, 214, 206, 178, 179, 205, 227,\n",
-              "          6,   0],\n",
-              "       [  3, 104, 128, 126, 124, 105,  85,  75,  99, 181, 140, 141, 185,\n",
-              "        179, 181, 183, 185, 204, 227, 255, 224, 226, 234, 227, 159, 198,\n",
-              "        198,   0],\n",
-              "       [ 62, 150, 115, 111,  91, 117, 131, 102, 129, 120, 153, 176, 216,\n",
-              "        232, 238, 238, 211, 242, 173,  26,   0,  54, 104,  20,   0,   0,\n",
-              "         48,  16],\n",
-              "       [116, 179, 173, 173, 202, 188, 200, 228, 179, 150, 100,  89, 164,\n",
-              "         39,   0,  82, 129,  17,   0,   0,   0,   0,   0,   0,  25,  11,\n",
-              "          0,  12],\n",
-              "       [ 10, 122, 108, 148, 115, 145, 148,  79,  38,   0,   0,   0,   0,\n",
-              "          0,   0,   0,   0,   0,   9,  14,   7,  18,   9,   5,   0,  12,\n",
-              "          0,   0],\n",
-              "       [  0,   5,  57,  67,   0,   0,   0,   1,   0,   0,   8,   8,  71,\n",
-              "         55,  11,  32,  30,  30,  17,   4,   3,   9,  15,  17,  29,  37,\n",
-              "         92, 106],\n",
-              "       [  0,   0,   0,  58,  50, 142, 169,  80, 144, 156, 148, 159, 152,\n",
-              "        156, 136, 151, 142,  70, 139, 161,  98, 131, 145,  60, 141, 121,\n",
-              "         77,   0],\n",
-              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
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-              "          0,   0],\n",
-              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
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-              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-              "          0,   0],\n",
-              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-              "          0,   0],\n",
-              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
-              "          0,   0]], dtype=uint8)</pre></div><script>\n",
-              "      (() => {\n",
-              "      const titles = ['show data', 'hide data'];\n",
-              "      let index = 0\n",
-              "      document.querySelector('#id-756e8e70-8f2b-4994-bc51-1a7ad20c9bc2 button').onclick = (e) => {\n",
-              "        document.querySelector('#id-756e8e70-8f2b-4994-bc51-1a7ad20c9bc2').classList.toggle('show_array');\n",
-              "        index = (++index) % 2;\n",
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-              "        e.preventDefault();\n",
-              "        e.stopPropagation();\n",
-              "      }\n",
-              "      })();\n",
-              "    </script>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 23
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -621,29 +293,14 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "SIgbDeIbqELs",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "9c1efbcc-b259-4093-c675-26779293ce39"
+        "id": "SIgbDeIbqELs"
       },
       "source": [
         "number_of_classes = train_labels.max() + 1\n",
         "number_of_classes"
       ],
-      "execution_count": 24,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "np.uint8(10)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 24
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "code",
@@ -657,7 +314,7 @@
         "    tf.keras.layers.Dense(int(number_of_classes))\n",
         "])"
       ],
-      "execution_count": 30,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -674,96 +331,13 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "wYSHZNPEA1tb",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 193
-        },
-        "outputId": "85d482b0-286a-413a-f9a8-99d19325ca49"
+        "id": "wYSHZNPEA1tb"
       },
       "source": [
         "model.summary()"
       ],
-      "execution_count": 31,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
-            ],
-            "text/html": [
-              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
-              "</pre>\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
-              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-              "│ flatten_5 (\u001b[38;5;33mFlatten\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m7,850\u001b[0m │\n",
-              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-            ],
-            "text/html": [
-              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
-              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-              "│ flatten_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">784</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-              "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> │\n",
-              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-              "</pre>\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m7,850\u001b[0m (30.66 KB)\n"
-            ],
-            "text/html": [
-              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> (30.66 KB)\n",
-              "</pre>\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m7,850\u001b[0m (30.66 KB)\n"
-            ],
-            "text/html": [
-              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,850</span> (30.66 KB)\n",
-              "</pre>\n"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-            ],
-            "text/html": [
-              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
-              "</pre>\n"
-            ]
-          },
-          "metadata": {}
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -777,11 +351,7 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "00aNzMfrQARw",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "8e40f331-7b39-4001-fbb4-3dfaf7b592ea"
+        "id": "00aNzMfrQARw"
       },
       "source": [
         "image_height = 28\n",
@@ -790,19 +360,8 @@
         "number_of_weights = image_height * image_width * int(number_of_classes)\n",
         "number_of_weights"
       ],
-      "execution_count": 34,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "7840"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 34
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -818,30 +377,13 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "3wY9VLOKKyKw",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 498
-        },
-        "outputId": "0f8eeba3-c825-4122-e7a4-501f3873ba70"
+        "id": "3wY9VLOKKyKw"
       },
       "source": [
         "tf.keras.utils.plot_model(model, show_shapes=True)"
       ],
-      "execution_count": 35,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<IPython.core.display.Image object>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 35
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -894,7 +436,7 @@
         "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
         "              metrics=['accuracy'])"
       ],
-      "execution_count": 36,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -915,11 +457,7 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "A0adp_qwA4Kg",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "999eb875-80ea-44e0-900e-64ecd255c053"
+        "id": "A0adp_qwA4Kg"
       },
       "source": [
         "history = model.fit(\n",
@@ -930,25 +468,8 @@
         "    validation_data=(valid_images, valid_labels)\n",
         ")"
       ],
-      "execution_count": 38,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Epoch 1/5\n",
-            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.8044 - loss: 9.6828 - val_accuracy: 0.7917 - val_loss: 11.1440\n",
-            "Epoch 2/5\n",
-            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8054 - loss: 10.1757 - val_accuracy: 0.7772 - val_loss: 13.2141\n",
-            "Epoch 3/5\n",
-            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.8060 - loss: 9.9181 - val_accuracy: 0.7809 - val_loss: 11.4187\n",
-            "Epoch 4/5\n",
-            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8095 - loss: 9.4431 - val_accuracy: 0.7621 - val_loss: 17.4376\n",
-            "Epoch 5/5\n",
-            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.8048 - loss: 10.4435 - val_accuracy: 0.7898 - val_loss: 12.2043\n"
-          ]
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -972,64 +493,13 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "XcDRhkVWD168",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "7896cac5-dcef-4248-b2a3-7fbcbe0c41ea"
+        "id": "XcDRhkVWD168"
       },
       "source": [
         "model.predict(train_images[0:10])"
       ],
-      "execution_count": 39,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[ -406.26605 ,  -810.9323  ,  -245.22408 ,  -408.96628 ,\n",
-              "         -433.6929  ,   361.1237  ,  -176.61609 ,   338.9754  ,\n",
-              "           58.718292,   530.6108  ],\n",
-              "       [  218.37871 ,  -388.85202 ,    90.183464,   -24.636917,\n",
-              "         -214.07404 , -1627.0286  ,    35.187714, -2225.3076  ,\n",
-              "         -226.93773 , -1239.6675  ],\n",
-              "       [   70.7416  ,   -23.040031,    33.486485,    66.72533 ,\n",
-              "           13.267202,  -557.6654  ,    10.02778 ,  -575.08093 ,\n",
-              "          -99.35069 ,  -336.0345  ],\n",
-              "       [  162.024   ,    82.8766  ,   153.49939 ,   181.7207  ,\n",
-              "           67.74914 ,  -834.94104 ,   130.10194 , -1216.4783  ,\n",
-              "         -144.43387 ,  -827.5984  ],\n",
-              "       [  159.403   ,    68.476814,    76.09903 ,   171.12115 ,\n",
-              "           70.943825, -1045.7615  ,    36.534264,  -810.8569  ,\n",
-              "          -60.227158, -1148.3757  ],\n",
-              "       [   17.732721,  -173.01819 ,   262.7382  ,   -72.45895 ,\n",
-              "          108.93694 , -1121.0135  ,    41.596664, -1926.2325  ,\n",
-              "         -146.07317 , -1275.9569  ],\n",
-              "       [ -217.902   ,  -277.93948 ,  -154.54495 ,  -177.51439 ,\n",
-              "         -230.92055 ,   103.68795 ,  -197.71262 ,   253.50223 ,\n",
-              "           22.665834,    56.596657],\n",
-              "       [ -104.23269 ,  -303.87686 ,   225.72911 ,  -369.8115  ,\n",
-              "          234.82683 , -1584.358   ,   -59.033424, -2176.585   ,\n",
-              "         -134.48302 , -1218.0953  ],\n",
-              "       [  -58.612274,  -566.5974  ,  -137.47835 ,  -220.60683 ,\n",
-              "         -298.25684 ,   474.15652 ,   -93.56001 ,    33.72026 ,\n",
-              "         -102.94882 ,   202.78635 ],\n",
-              "       [  -78.46059 ,  -498.6948  ,    46.64348 ,  -197.15094 ,\n",
-              "         -112.02182 ,   868.1138  ,   -17.570368,   -21.103134,\n",
-              "          114.397484,   200.15775 ]], dtype=float32)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 39
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -1060,12 +530,7 @@
     {
       "cell_type": "code",
       "metadata": {
-        "id": "7wiFSYdcM4Nt",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 882
-        },
-        "outputId": "bd693768-6c60-44b3-f20c-1b528719778b"
+        "id": "7wiFSYdcM4Nt"
       },
       "source": [
         "data_idx = 8675 # The question number to study with. Feel free to change up to 59999.\n",
@@ -1084,43 +549,8 @@
         "\n",
         "print(\"correct answer:\", train_labels[data_idx])"
       ],
-      "execution_count": 40,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step\n"
-          ]
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ],
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "correct answer: 6\n"
-          ]
-        }
-      ]
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",

From 8755cbaab25fd2c434283ca8ed725d95aefdf0c8 Mon Sep 17 00:00:00 2001
From: johnwhitegm <johnwhitegm@gmail.com>
Date: Mon, 4 May 2026 21:06:28 -0400
Subject: [PATCH 04/11] Created using Colab

---
 building-a-brain/BuildingABrain.ipynb | 44 +++++++++++++++++++--------
 1 file changed, 32 insertions(+), 12 deletions(-)

diff --git a/building-a-brain/BuildingABrain.ipynb b/building-a-brain/BuildingABrain.ipynb
index d10ec62..6b4f1c3 100644
--- a/building-a-brain/BuildingABrain.ipynb
+++ b/building-a-brain/BuildingABrain.ipynb
@@ -40,22 +40,42 @@
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "Jj9625DV7oKS"
-      },
       "source": [
         "from IPython.display import HTML\n",
         "\n",
-        "HTML(\"\"\"\n",
-        "<iframe width=\"560\" height=\"315\"\n",
-        "src=\"https://www.youtube.com/embed/cNxadbrN_aI\"\n",
-        "frameborder=\"0\"\n",
-        "allowfullscreen\n",
-        "referrerpolicy=\"strict-origin-when-cross-origin\"></iframe>\n",
-        "\"\"\")"
+        "HTML(\n",
+        "    '<iframe width=\"560\" height=\"315\" '\n",
+        "    'src=\"https://www.youtube.com/embed/cNxadbrN_aI\" '\n",
+        "    'frameborder=\"0\" '\n",
+        "    'allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" '\n",
+        "    'allowfullscreen '\n",
+        "    'referrerpolicy=\"strict-origin-when-cross-origin\"></iframe>'\n",
+        ")"
       ],
-      "execution_count": null,
-      "outputs": []
+      "metadata": {
+        "id": "viMxZGykqyK6",
+        "outputId": "33740c4d-6c52-4a1c-cb7d-78bd98efd637",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 336
+        }
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/cNxadbrN_aI\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen referrerpolicy=\"strict-origin-when-cross-origin\"></iframe>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
     },
     {
       "cell_type": "markdown",

From 9949fb9a9e6a9518bed56ac8d954e252da96b66d Mon Sep 17 00:00:00 2001
From: johnwhitegm <johnwhitegm@gmail.com>
Date: Mon, 4 May 2026 21:07:44 -0400
Subject: [PATCH 05/11] Created using Colab

---
 building-a-brain/BuildingABrain.ipynb | 31 ++++++++++++++++++---------
 1 file changed, 21 insertions(+), 10 deletions(-)

diff --git a/building-a-brain/BuildingABrain.ipynb b/building-a-brain/BuildingABrain.ipynb
index 6b4f1c3..c81fd95 100644
--- a/building-a-brain/BuildingABrain.ipynb
+++ b/building-a-brain/BuildingABrain.ipynb
@@ -43,24 +43,35 @@
       "source": [
         "from IPython.display import HTML\n",
         "\n",
-        "HTML(\n",
-        "    '<iframe width=\"560\" height=\"315\" '\n",
-        "    'src=\"https://www.youtube.com/embed/cNxadbrN_aI\" '\n",
+        "video_id = \"cNxadbrN_aI\"\n",
+        "width = 560\n",
+        "height = 315\n",
+        "\n",
+        "src = f\"https://www.youtube.com/embed/{video_id}\"\n",
+        "\n",
+        "iframe = (\n",
+        "    \"<iframe \"\n",
+        "    f'width=\"{width}\" '\n",
+        "    f'height=\"{height}\" '\n",
+        "    f'src=\"{src}\" '\n",
         "    'frameborder=\"0\" '\n",
         "    'allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" '\n",
         "    'allowfullscreen '\n",
-        "    'referrerpolicy=\"strict-origin-when-cross-origin\"></iframe>'\n",
-        ")"
+        "    'referrerpolicy=\"strict-origin-when-cross-origin\">'\n",
+        "    \"</iframe>\"\n",
+        ")\n",
+        "\n",
+        "HTML(iframe)"
       ],
       "metadata": {
-        "id": "viMxZGykqyK6",
-        "outputId": "33740c4d-6c52-4a1c-cb7d-78bd98efd637",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 336
-        }
+        },
+        "id": "viMxZGykqyK6",
+        "outputId": "2c19942f-0f86-4b94-8323-79d92bce85d0"
       },
-      "execution_count": 2,
+      "execution_count": 3,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -73,7 +84,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 2
+          "execution_count": 3
         }
       ]
     },

From f39c33a0d26a671caafb78306e063de6a6acad29 Mon Sep 17 00:00:00 2001
From: johnwhitegm <johnwhitegm@gmail.com>
Date: Mon, 4 May 2026 21:09:50 -0400
Subject: [PATCH 06/11] Created using Colab

---
 building-a-brain/BuildingABrain.ipynb | 49 +++++++--------------------
 1 file changed, 13 insertions(+), 36 deletions(-)

diff --git a/building-a-brain/BuildingABrain.ipynb b/building-a-brain/BuildingABrain.ipynb
index c81fd95..be2aec3 100644
--- a/building-a-brain/BuildingABrain.ipynb
+++ b/building-a-brain/BuildingABrain.ipynb
@@ -47,46 +47,23 @@
         "width = 560\n",
         "height = 315\n",
         "\n",
-        "src = f\"https://www.youtube.com/embed/{video_id}\"\n",
-        "\n",
-        "iframe = (\n",
-        "    \"<iframe \"\n",
-        "    f'width=\"{width}\" '\n",
-        "    f'height=\"{height}\" '\n",
-        "    f'src=\"{src}\" '\n",
-        "    'frameborder=\"0\" '\n",
-        "    'allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" '\n",
-        "    'allowfullscreen '\n",
-        "    'referrerpolicy=\"strict-origin-when-cross-origin\">'\n",
-        "    \"</iframe>\"\n",
-        ")\n",
+        "iframe = f\"\"\"\n",
+        "<iframe width=\"{width}\" height=\"{height}\"\n",
+        "src=\"https://www.youtube.com/embed/{video_id}\"\n",
+        "frameborder=\"0\"\n",
+        "allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\"\n",
+        "allowfullscreen\n",
+        "referrerpolicy=\"strict-origin-when-cross-origin\">\n",
+        "</iframe>\n",
+        "\"\"\"\n",
         "\n",
         "HTML(iframe)"
       ],
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 336
-        },
-        "id": "viMxZGykqyK6",
-        "outputId": "2c19942f-0f86-4b94-8323-79d92bce85d0"
-      },
-      "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/cNxadbrN_aI\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen referrerpolicy=\"strict-origin-when-cross-origin\"></iframe>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 3
-        }
-      ]
+        "id": "viMxZGykqyK6"
+      },
+      "execution_count": null,
+      "outputs": []
     },
     {
       "cell_type": "markdown",

From 4d832428b35fad6fde53a2171c2d1f3a824c5bea Mon Sep 17 00:00:00 2001
From: johnwhitegm <johnwhitegm@gmail.com>
Date: Mon, 4 May 2026 21:11:05 -0400
Subject: [PATCH 07/11] Created using Colab

---
 building-a-brain/BuildingABrain.ipynb | 26 ++++++++++++--------------
 1 file changed, 12 insertions(+), 14 deletions(-)

diff --git a/building-a-brain/BuildingABrain.ipynb b/building-a-brain/BuildingABrain.ipynb
index be2aec3..74ef79b 100644
--- a/building-a-brain/BuildingABrain.ipynb
+++ b/building-a-brain/BuildingABrain.ipynb
@@ -44,20 +44,18 @@
         "from IPython.display import HTML\n",
         "\n",
         "video_id = \"cNxadbrN_aI\"\n",
-        "width = 560\n",
-        "height = 315\n",
-        "\n",
-        "iframe = f\"\"\"\n",
-        "<iframe width=\"{width}\" height=\"{height}\"\n",
-        "src=\"https://www.youtube.com/embed/{video_id}\"\n",
-        "frameborder=\"0\"\n",
-        "allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\"\n",
-        "allowfullscreen\n",
-        "referrerpolicy=\"strict-origin-when-cross-origin\">\n",
-        "</iframe>\n",
-        "\"\"\"\n",
-        "\n",
-        "HTML(iframe)"
+        "\n",
+        "html = (\n",
+        "    f'<iframe width=\"560\" height=\"315\" '\n",
+        "    f'src=\"https://www.youtube.com/embed/{video_id}\" '\n",
+        "    f'frameborder=\"0\" '\n",
+        "    f'allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" '\n",
+        "    f'allowfullscreen '\n",
+        "    f'referrerpolicy=\"strict-origin-when-cross-origin\">'\n",
+        "    f'</iframe>'\n",
+        ")\n",
+        "\n",
+        "HTML(html)"
       ],
       "metadata": {
         "id": "viMxZGykqyK6"

From 1d1332d33a3b776d53ebccf335a6c75ae5d6fc41 Mon Sep 17 00:00:00 2001
From: johnwhitegm <johnwhitegm@gmail.com>
Date: Mon, 4 May 2026 21:14:36 -0400
Subject: [PATCH 08/11] Created using Colab

---
 building-a-brain/BuildingABrain.ipynb | 11 ++---------
 1 file changed, 2 insertions(+), 9 deletions(-)

diff --git a/building-a-brain/BuildingABrain.ipynb b/building-a-brain/BuildingABrain.ipynb
index 74ef79b..b5af685 100644
--- a/building-a-brain/BuildingABrain.ipynb
+++ b/building-a-brain/BuildingABrain.ipynb
@@ -44,16 +44,9 @@
         "from IPython.display import HTML\n",
         "\n",
         "video_id = \"cNxadbrN_aI\"\n",
+        "src = \"https://www.youtube.com/embed/\" + video_id\n",
         "\n",
-        "html = (\n",
-        "    f'<iframe width=\"560\" height=\"315\" '\n",
-        "    f'src=\"https://www.youtube.com/embed/{video_id}\" '\n",
-        "    f'frameborder=\"0\" '\n",
-        "    f'allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" '\n",
-        "    f'allowfullscreen '\n",
-        "    f'referrerpolicy=\"strict-origin-when-cross-origin\">'\n",
-        "    f'</iframe>'\n",
-        ")\n",
+        "html = '<iframe width=\"560\" height=\"315\" src=\"' + src + '\" frameborder=\"0\" allowfullscreen referrerpolicy=\"strict-origin-when-cross-origin\"></iframe>'\n",
         "\n",
         "HTML(html)"
       ],

From 527216a2e224f14c7ae12d264d8e215cfd33f5fb Mon Sep 17 00:00:00 2001
From: johnwhitegm <johnwhitegm@gmail.com>
Date: Mon, 4 May 2026 21:15:53 -0400
Subject: [PATCH 09/11] Created using Colab

---
 building-a-brain/BuildingABrain.ipynb | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/building-a-brain/BuildingABrain.ipynb b/building-a-brain/BuildingABrain.ipynb
index b5af685..45e46fc 100644
--- a/building-a-brain/BuildingABrain.ipynb
+++ b/building-a-brain/BuildingABrain.ipynb
@@ -46,7 +46,11 @@
         "video_id = \"cNxadbrN_aI\"\n",
         "src = \"https://www.youtube.com/embed/\" + video_id\n",
         "\n",
-        "html = '<iframe width=\"560\" height=\"315\" src=\"' + src + '\" frameborder=\"0\" allowfullscreen referrerpolicy=\"strict-origin-when-cross-origin\"></iframe>'\n",
+        "html = (\n",
+        "    '<iframe width=\"560\" height=\"315\" src=\"' + src + '\" '\n",
+        "    'frameborder=\"0\" allowfullscreen '\n",
+        "    'referrerpolicy=\"strict-origin-when-cross-origin\"></iframe>'\n",
+        ")\n",
         "\n",
         "HTML(html)"
       ],

From b7110e363c1a19cf13125790f922374a0f05b02e Mon Sep 17 00:00:00 2001
From: johnwhitegm <johnwhitegm@gmail.com>
Date: Mon, 4 May 2026 21:17:32 -0400
Subject: [PATCH 10/11] Created using Colab

---
 building-a-brain/BuildingABrain.ipynb | 14 +++++++++++---
 1 file changed, 11 insertions(+), 3 deletions(-)

diff --git a/building-a-brain/BuildingABrain.ipynb b/building-a-brain/BuildingABrain.ipynb
index 45e46fc..ca58ea2 100644
--- a/building-a-brain/BuildingABrain.ipynb
+++ b/building-a-brain/BuildingABrain.ipynb
@@ -46,10 +46,18 @@
         "video_id = \"cNxadbrN_aI\"\n",
         "src = \"https://www.youtube.com/embed/\" + video_id\n",
         "\n",
+        "tag_open = \"<\" + \"iframe\"\n",
+        "tag_close = \"<\" + \"/\" + \"iframe\" + \">\"\n",
+        "\n",
         "html = (\n",
-        "    '<iframe width=\"560\" height=\"315\" src=\"' + src + '\" '\n",
-        "    'frameborder=\"0\" allowfullscreen '\n",
-        "    'referrerpolicy=\"strict-origin-when-cross-origin\"></iframe>'\n",
+        "    tag_open\n",
+        "    + ' width=\"560\" height=\"315\"'\n",
+        "    + ' src=\"' + src + '\"'\n",
+        "    + ' frameborder=\"0\"'\n",
+        "    + ' allowfullscreen'\n",
+        "    + ' referrerpolicy=\"strict-origin-when-cross-origin\"'\n",
+        "    + \">\"\n",
+        "    + tag_close\n",
         ")\n",
         "\n",
         "HTML(html)"

From 91630d1f0e5e5dfc249327ca7aa8b51db23b50be Mon Sep 17 00:00:00 2001
From: johnwhitegm <johnwhitegm@gmail.com>
Date: Mon, 4 May 2026 21:53:33 -0400
Subject: [PATCH 11/11] Update README.md

---
 README.md | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/README.md b/README.md
index 1041a44..bc482f7 100644
--- a/README.md
+++ b/README.md
@@ -13,3 +13,13 @@ This repository contains notebooks authored by the [NVIDIA DLI](https://nvidia.c
 ## NVIDIA DLI Catalog
 
 If you enjoy these notebooks, we recommend you check out the [DLI's full catalog of courses](nvidia.com/dli) which cover a much broader range of topics, in much greater depth, with dedicated GPU resources and a more sophisticated programming environment.
+
+## Notes
+
+This fork includes fixes for:
+- Colab YouTube embed issues (Error 153)
+- Image rendering inconsistencies
+- GitHub notebook preview compatibility
+- Keras model initialization errors
+
+See pull request for full details.