diff --git "a/notebook.ipynb" "b/notebook.ipynb"
--- "a/notebook.ipynb"
+++ "b/notebook.ipynb"
@@ -6,7 +6,7 @@
    "source": [
     "## How does a neural net really work\n",
     "\n",
-    "In this notebook I'm exploring fast.ai's Kaggle notebook on [\"How does a neural net really work\"](https://www.kaggle.com/code/jhoward/how-does-a-neural-net-really-work). This relates to [Lesson 3 of the fast.ai Deep Learning course](https://course.fast.ai/Lessons/lesson3.html). While the video provides a solid explanation, the enigmatic imports and variables can be difficult to comprehend. I'm reimplementing some sections to see if if sticks. In a nutshell, this is what is happening in this notebook:\n",
+    "In this notebook I'm exploring fast.ai's Kaggle notebook on [\"How does a neural net really work\"](https://www.kaggle.com/code/jhoward/how-does-a-neural-net-really-work). This relates to [Lesson 3](https://course.fast.ai/Lessons/lesson3.html) and [Lesson 5](https://course.fast.ai/Lessons/lesson5.html) of the fast.ai Deep Learning course. While the video provides a solid explanation, the enigmatic imports and variables can be difficult to comprehend. I'm reimplementing some sections to see if if sticks. In a nutshell, this is what is happening in this notebook:\n",
     "\n",
     "1. Revising Regressions  \n",
     "   - Plot a generic quadratic function ($ax^2 + bx + c$)  \n",
@@ -14,9 +14,10 @@
     "   - Learn the step-by-step process to find the values of `a`, `b`, and `c` that make our function represent the random data generated in `2`  \n",
     "   - Use the [mean absolute error](https://docs.fast.ai/metrics.html#mae) to manually adjust `a`, `b`, and `c`.  \n",
     "2. Understand and break down the Gradient Descent algorithm\n",
-    "3. The Basics of a Neural-Network\n",
-    "   - Understand what is a ReLU\n",
-    "   - Create the simplest neural-network possible"
+    "3. The Basics of a Neural-Network using the Titanic Survival dataset form Kaggle\n",
+    "   - Explore ReLUs and how it differs from a simpel linear function\n",
+    "   - Build a single-layer neural network using a simple linear function $f(x) = m*x$ (m being a array of weights that we multiply by our features)\n",
+    "   - Do Deep Learning by layering coefficients/wheights/neurons to do a multi-layer neural network"
    ]
   },
   {
@@ -24,10 +25,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
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-     "shell.execute_reply": "2025-02-28T14:26:02.705945Z"
+     "iopub.execute_input": "2025-03-04T16:43:18.516396Z",
+     "iopub.status.busy": "2025-03-04T16:43:18.515931Z",
+     "iopub.status.idle": "2025-03-04T16:43:20.469155Z",
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    "outputs": [
@@ -41,14 +42,17 @@
    ],
    "source": [
     "# Installing the dependencies within the notebook to make it easier to run on colab\n",
-    "%pip install -Uqq fastai==2.7.18 ipywidgets==8.1.5 plotly==5.24.1"
+    "%pip install -Uqq fastai==2.7.18 ipywidgets==8.1.5 plotly==5.24.1 datasets==3.3.2"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 1. Revising Regressions"
+    "## 1. Revising Regressions\n",
+    " \n",
+    "This section, from the fast.ai course, sets the stage for understanding how neural networks learn \"weights\". \n",
+    "We'll plot some points on a graphic and use visualizations to see how changing the coefficients affects the function to better fit the points."
    ]
   },
   {
@@ -63,15 +67,21 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
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-     "iopub.status.busy": "2025-02-28T14:26:02.741000Z",
-     "iopub.status.idle": "2025-02-28T14:26:04.442990Z",
-     "shell.execute_reply": "2025-02-28T14:26:04.442698Z"
+     "iopub.execute_input": "2025-03-04T16:43:20.502799Z",
+     "iopub.status.busy": "2025-03-04T16:43:20.502615Z",
+     "iopub.status.idle": "2025-03-04T16:43:22.296560Z",
+     "shell.execute_reply": "2025-03-04T16:43:22.296267Z"
     }
    },
    "outputs": [],
    "source": [
     "from fastai.basics import torch, plt\n",
+    "import numpy as np, pandas as pd\n",
+    "\n",
+    "# Make pandas and numpy use the entire screan\n",
+    "np.set_printoptions(linewidth=140)\n",
+    "torch.set_printoptions(linewidth=140, sci_mode=False, edgeitems=7)\n",
+    "pd.set_option('display.width', 140)\n",
     "\n",
     "# Set the figure DPI to 90 for better resolution\n",
     "plt.rc('figure', dpi=90)\n",
@@ -93,10 +103,10 @@
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-     "shell.execute_reply": "2025-02-28T14:26:04.565045Z"
+     "iopub.execute_input": "2025-03-04T16:43:22.298117Z",
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+     "iopub.status.idle": "2025-03-04T16:43:22.420309Z",
+     "shell.execute_reply": "2025-03-04T16:43:22.420023Z"
     }
    },
    "outputs": [
@@ -142,10 +152,10 @@
    "execution_count": 4,
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-     "shell.execute_reply": "2025-02-28T14:26:04.618081Z"
+     "iopub.execute_input": "2025-03-04T16:43:22.421798Z",
+     "iopub.status.busy": "2025-03-04T16:43:22.421679Z",
+     "iopub.status.idle": "2025-03-04T16:43:22.473127Z",
+     "shell.execute_reply": "2025-03-04T16:43:22.472853Z"
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    "outputs": [
@@ -197,17 +207,17 @@
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-     "shell.execute_reply": "2025-02-28T14:26:04.700518Z"
+     "iopub.execute_input": "2025-03-04T16:43:22.474508Z",
+     "iopub.status.busy": "2025-03-04T16:43:22.474417Z",
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+     "shell.execute_reply": "2025-03-04T16:43:22.603715Z"
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@@ -249,10 +259,10 @@
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-     "shell.execute_reply": "2025-02-28T14:26:04.705648Z"
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@@ -296,17 +306,17 @@
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-     "shell.execute_reply": "2025-02-28T14:26:04.756430Z"
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@@ -357,10 +367,10 @@
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@@ -12040,9 +12050,9 @@
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        "                            \n",
-       "var gd = document.getElementById('da54766d-b33f-42b6-a6a4-c0830e541b57');\n",
+       "var gd = document.getElementById('96242666-faaa-4a98-bf73-87b97420760a');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -12170,10 +12180,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2025-02-28T14:26:05.109770Z",
-     "iopub.status.busy": "2025-02-28T14:26:05.109618Z",
-     "iopub.status.idle": "2025-02-28T14:26:05.125709Z",
-     "shell.execute_reply": "2025-02-28T14:26:05.125415Z"
+     "iopub.execute_input": "2025-03-04T16:43:22.938924Z",
+     "iopub.status.busy": "2025-03-04T16:43:22.938776Z",
+     "iopub.status.idle": "2025-03-04T16:43:22.955190Z",
+     "shell.execute_reply": "2025-03-04T16:43:22.954907Z"
     }
    },
    "outputs": [
@@ -12307,17 +12317,17 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2025-02-28T14:26:05.127168Z",
-     "iopub.status.busy": "2025-02-28T14:26:05.127049Z",
-     "iopub.status.idle": "2025-02-28T14:26:05.177580Z",
-     "shell.execute_reply": "2025-02-28T14:26:05.177312Z"
+     "iopub.execute_input": "2025-03-04T16:43:22.956589Z",
+     "iopub.status.busy": "2025-03-04T16:43:22.956470Z",
+     "iopub.status.idle": "2025-03-04T16:43:23.007065Z",
+     "shell.execute_reply": "2025-03-04T16:43:23.006815Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8eb3c197199c48efb68b11c5bfa516f2",
+       "model_id": "baf5304da5374d4a92871ae0d0974507",
        "version_major": 2,
        "version_minor": 0
       },
@@ -12356,10 +12366,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2025-02-28T14:26:05.179676Z",
-     "iopub.status.busy": "2025-02-28T14:26:05.179586Z",
-     "iopub.status.idle": "2025-02-28T14:26:05.200792Z",
-     "shell.execute_reply": "2025-02-28T14:26:05.200511Z"
+     "iopub.execute_input": "2025-03-04T16:43:23.009179Z",
+     "iopub.status.busy": "2025-03-04T16:43:23.009082Z",
+     "iopub.status.idle": "2025-03-04T16:43:23.019707Z",
+     "shell.execute_reply": "2025-03-04T16:43:23.019446Z"
     }
    },
    "outputs": [
@@ -12387,44 +12397,14 @@
       "step=17; loss=0.74; abc=tensor([2.8330, 1.5484, 1.3900], requires_grad=True)\n",
       "step=18; loss=0.71; abc=tensor([2.8771, 1.5611, 1.3700], requires_grad=True)\n",
       "step=19; loss=0.69; abc=tensor([2.9212, 1.5737, 1.3500], requires_grad=True)\n",
-      "step=20; loss=0.68; abc=tensor([2.9155, 1.5863, 1.3100], requires_grad=True)\n",
-      "step=21; loss=0.66; abc=tensor([2.9346, 1.5832, 1.2800], requires_grad=True)\n",
-      "step=22; loss=0.65; abc=tensor([2.9289, 1.5958, 1.2400], requires_grad=True)\n",
-      "step=23; loss=0.64; abc=tensor([2.9480, 1.5926, 1.2100], requires_grad=True)\n",
-      "step=24; loss=0.62; abc=tensor([2.9672, 1.5895, 1.1800], requires_grad=True)\n",
-      "step=25; loss=0.61; abc=tensor([2.9864, 1.5863, 1.1500], requires_grad=True)\n",
-      "step=26; loss=0.60; abc=tensor([2.9807, 1.6000, 1.1200], requires_grad=True)\n",
-      "step=27; loss=0.60; abc=tensor([3.0064, 1.5937, 1.1200], requires_grad=True)\n",
-      "step=28; loss=0.59; abc=tensor([3.0018, 1.6105, 1.1000], requires_grad=True)\n",
-      "step=29; loss=0.59; abc=tensor([3.0275, 1.6042, 1.1000], requires_grad=True)\n",
-      "step=30; loss=0.59; abc=tensor([3.0283, 1.6137, 1.0900], requires_grad=True)\n",
-      "step=31; loss=0.58; abc=tensor([3.0290, 1.6232, 1.0800], requires_grad=True)\n",
-      "step=32; loss=0.58; abc=tensor([3.0547, 1.6168, 1.0800], requires_grad=True)\n",
-      "step=33; loss=0.58; abc=tensor([3.0555, 1.6263, 1.0700], requires_grad=True)\n",
-      "step=34; loss=0.58; abc=tensor([3.0563, 1.6358, 1.0600], requires_grad=True)\n",
-      "step=35; loss=0.58; abc=tensor([3.0571, 1.6453, 1.0500], requires_grad=True)\n",
-      "step=36; loss=0.58; abc=tensor([3.0828, 1.6389, 1.0500], requires_grad=True)\n",
-      "step=37; loss=0.58; abc=tensor([3.0835, 1.6484, 1.0400], requires_grad=True)\n",
-      "step=38; loss=0.58; abc=tensor([3.0843, 1.6579, 1.0300], requires_grad=True)\n",
-      "step=39; loss=0.57; abc=tensor([3.0851, 1.6674, 1.0200], requires_grad=True)\n",
-      "step=40; loss=0.57; abc=tensor([3.1136, 1.6558, 1.0300], requires_grad=True)\n",
-      "step=41; loss=0.58; abc=tensor([3.0956, 1.6516, 1.0100], requires_grad=True)\n",
-      "step=42; loss=0.57; abc=tensor([3.0992, 1.6558, 1.0100], requires_grad=True)\n",
-      "step=43; loss=0.57; abc=tensor([3.1027, 1.6600, 1.0100], requires_grad=True)\n",
-      "step=44; loss=0.57; abc=tensor([3.1063, 1.6642, 1.0100], requires_grad=True)\n",
-      "step=45; loss=0.57; abc=tensor([3.0911, 1.6547, 1.0000], requires_grad=True)\n",
-      "step=46; loss=0.57; abc=tensor([3.0946, 1.6589, 1.0000], requires_grad=True)\n",
-      "step=47; loss=0.57; abc=tensor([3.0982, 1.6632, 1.0000], requires_grad=True)\n",
-      "step=48; loss=0.57; abc=tensor([3.1017, 1.6674, 1.0000], requires_grad=True)\n",
-      "step=49; loss=0.57; abc=tensor([3.1053, 1.6716, 1.0000], requires_grad=True)\n",
-      "Best abc parameters: tensor([3.1053, 1.6716, 1.0000])\n"
+      "Best abc parameters: tensor([2.9212, 1.5737, 1.3500])\n"
      ]
     }
    ],
    "source": [
     "from fastai.metrics import mae\n",
     "\n",
-    "def demo_auto_fit(steps=50):\n",
+    "def demo_auto_fit(steps=20):\n",
     "    x, y = generate_noisy_data(mk_quad(3,2,1))\n",
     "\n",
     "    abc = torch.tensor([1.0,1.0,1.0], requires_grad=True)\n",
@@ -12451,177 +12431,2062 @@
     "best_abc_params = demo_auto_fit()\n",
     "print(f\"Best abc parameters: {best_abc_params}\")"
    ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
   },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. The Basics of a Neural-Network"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1 Introducing Non-Linearity with ReLU\n",
+    "\n",
+    "We've seen that simple functions like quadratics can model some data, but real-world data is rarely so straightforward.  Imagine trying to predict something complex, like whether a picture is a cat or a dog, based on many pixel values (our 'dimensions').  A simple quadratic or even a single linear function just won't be flexible enough to capture the intricate patterns in such high-dimensional data.  \n",
+    "\n",
+    "To handle this complexity, we need to build more powerful functions.  Simply combining linear functions won't solve the problem because any combination of linear functions is still just a linear function!  Linear functions can only model linear relationships in the data.  Real-world data, like images of cats and dogs, is highly non-linear.\n",
+    "\n",
+    "To introduce non-linearity, we use activation functions.  ReLU (Rectified Linear Unit) is a simple yet powerful activation function that introduces non-linearity.  By applying ReLU to the output of linear functions, we can create models that can learn complex, non-linear patterns in the data. This non-linearity is what allows neural networks to model intricate relationships that simple linear models cannot.  This will lead us to the idea of a `ReLU`, a simple activation function, and the simplest \"neural network\" we can build with it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:23.021103Z",
+     "iopub.status.busy": "2025-03-04T16:43:23.021015Z",
+     "iopub.status.idle": "2025-03-04T16:43:23.059731Z",
+     "shell.execute_reply": "2025-03-04T16:43:23.059473Z"
+    }
    },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.16"
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def rectified_linear(m,b,x):\n",
+    "    y = m*x+b\n",
+    "    return torch.clip(y, 0.)\n",
+    "\n",
+    "plot_function(partial(rectified_linear, 1,1))"
+   ]
   },
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-    "state": {
-     "06520c9eb81f40c99fb7c962851ccbd0": {
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+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Combining two ReLUs allows us to create more complex, piecewise linear functions, as illustrated in the interactive plot below. This combination increases the flexibility of our model, enabling it to capture more intricate relationships in the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:23.061175Z",
+     "iopub.status.busy": "2025-03-04T16:43:23.061081Z",
+     "iopub.status.idle": "2025-03-04T16:43:23.100760Z",
+     "shell.execute_reply": "2025-03-04T16:43:23.100504Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "daa10b2c84ad4a9e8384392e574fe11f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=-1.5, description='m1', max=1.5, min=-4.5), FloatSlider(value=-1.5, de…"
+      ]
      },
-     "3cfe4f3a273d45e69aa5f9cf2134b68a": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "2.0.0",
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-       "grid_auto_flow": null,
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-      }
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def double_relu(m1,b1,m2,b2,x):\n",
+    "    return rectified_linear(m1,b1,x) + rectified_linear(m2,b2,x)\n",
+    "\n",
+    "@interact(m1=-1.5, b1=-1.5, m2=1.5, b2=1.5)\n",
+    "def plot_double_relu(m1, b1, m2, b2):\n",
+    "    plot_function(partial(double_relu, m1,b1,m2,b2), ylim=(-1,6))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2 Building a Neural Network from-Scratch\n",
+    "\n",
+    "From this point forward, we will be following this notebook: [Linear model and neural net from scratch](https://www.kaggle.com/code/jhoward/linear-model-and-neural-net-from-scratch). \n",
+    "\n",
+    "Important ⚠️: For simplicity, I'm skipping all the steps that involve data cleanup and preparation. This means of course that my model will most likely not have a very good performance.\n",
+    "\n",
+    "We are using the [Titanic competition from Kaggle](https://www.kaggle.com/competitions/titanic/data). I have made a copy in my [Hugging Face workspace](https://huggingface.co/datasets/paulopontesm/titanic/viewer/), which tbh I did to to experiment how Datasets work on Hugging Face.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "The goal is to create a model to predict whether a passenger `Survived`, which is provided in our dataset.\n",
+    "\n",
+    "In essence, we will now combine functions like those we've explored above, such as ReLUs, to construct a simple neural network. This network will receive passenger features as input, apply weights (similar to $m$ in our previous examples), and hopefully predict whether the passenger `Survived` with the lowest possible loss/error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:23.102668Z",
+     "iopub.status.busy": "2025-03-04T16:43:23.102579Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.757062Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.756703Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.250000</td>\n",
+       "      <td>None</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Thayer)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.283302</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.925000</td>\n",
+       "      <td>None</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.099998</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.050000</td>\n",
+       "      <td>None</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>886</th>\n",
+       "      <td>887</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Montvila, Rev. Juozas</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>211536</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>None</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>887</th>\n",
+       "      <td>888</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Graham, Miss. Margaret Edith</td>\n",
+       "      <td>female</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112053</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>B42</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>889</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>W./C. 6607</td>\n",
+       "      <td>23.450001</td>\n",
+       "      <td>None</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>890</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Behr, Mr. Karl Howell</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>111369</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>C148</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>890</th>\n",
+       "      <td>891</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Dooley, Mr. Patrick</td>\n",
+       "      <td>male</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>370376</td>\n",
+       "      <td>7.750000</td>\n",
+       "      <td>None</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>891 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     PassengerId  Survived  Pclass                                                 Name     Sex   Age  SibSp  Parch            Ticket  \\\n",
+       "0              1         0       3                              Braund, Mr. Owen Harris    male  22.0      1      0         A/5 21171   \n",
+       "1              2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Thayer)  female  38.0      1      0          PC 17599   \n",
+       "2              3         1       3                               Heikkinen, Miss. Laina  female  26.0      0      0  STON/O2. 3101282   \n",
+       "3              4         1       1         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1      0            113803   \n",
+       "4              5         0       3                             Allen, Mr. William Henry    male  35.0      0      0            373450   \n",
+       "..           ...       ...     ...                                                  ...     ...   ...    ...    ...               ...   \n",
+       "886          887         0       2                                Montvila, Rev. Juozas    male  27.0      0      0            211536   \n",
+       "887          888         1       1                         Graham, Miss. Margaret Edith  female  19.0      0      0            112053   \n",
+       "888          889         0       3             Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1      2        W./C. 6607   \n",
+       "889          890         1       1                                Behr, Mr. Karl Howell    male  26.0      0      0            111369   \n",
+       "890          891         0       3                                  Dooley, Mr. Patrick    male  32.0      0      0            370376   \n",
+       "\n",
+       "          Fare Cabin Embarked  \n",
+       "0     7.250000  None        S  \n",
+       "1    71.283302   C85        C  \n",
+       "2     7.925000  None        S  \n",
+       "3    53.099998  C123        S  \n",
+       "4     8.050000  None        S  \n",
+       "..         ...   ...      ...  \n",
+       "886  13.000000  None        S  \n",
+       "887  30.000000   B42        S  \n",
+       "888  23.450001  None        S  \n",
+       "889  30.000000  C148        C  \n",
+       "890   7.750000  None        Q  \n",
+       "\n",
+       "[891 rows x 12 columns]"
+      ]
      },
-     "3d5be33c44d04f4cb53c6a3746303852": {
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-       "grid_gap": null,
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-       "max_height": null,
-       "max_width": null,
-       "min_height": null,
-       "min_width": null,
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "# Load only train and test splits.\n",
+    "dataset = load_dataset(\"paulopontesm/titanic\", data_files={\"train\": \"train.csv\", \"test\": \"test.csv\"})\n",
+    "# Access the splits\n",
+    "train_dataset_df = dataset[\"train\"].to_pandas()\n",
+    "test_dataset_df = dataset[\"test\"].to_pandas()\n",
+    "\n",
+    "train_dataset_df\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since we need numerical data for our model, we'll just use the columns that already contain numbers as predictors. These are the columns that are already numerical."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.758898Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.758666Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.769639Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.769320Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>714.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.383838</td>\n",
+       "      <td>2.308642</td>\n",
+       "      <td>29.699118</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>32.204208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>257.353842</td>\n",
+       "      <td>0.486592</td>\n",
+       "      <td>0.836071</td>\n",
+       "      <td>14.526497</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>49.693432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>223.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>20.125000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.910400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.454200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>668.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>512.329224</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       PassengerId    Survived      Pclass         Age       SibSp       Parch        Fare\n",
+       "count   891.000000  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
+       "mean    446.000000    0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
+       "std     257.353842    0.486592    0.836071   14.526497    1.102743    0.806057   49.693432\n",
+       "min       1.000000    0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
+       "25%     223.500000    0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
+       "50%     446.000000    0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
+       "75%     668.500000    1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
+       "max     891.000000    1.000000    3.000000   80.000000    8.000000    6.000000  512.329224"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "train_dataset_df.describe(include=(np.number))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have numbers for the features, we can create tensors/arrays for our features (aka called independent variables) and target (aka dependent variable).\n",
+    "\n",
+    "Even thought I mentioned above that I didn't want to do a lot of data transofrmations, I think we really need to remove the NaNs and to normalize the numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.771195Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.771060Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.778599Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.778312Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0.2750, 0.1250, 0.0000, 0.0142],\n",
+       "        [0.4750, 0.1250, 0.0000, 0.1391],\n",
+       "        [0.3250, 0.0000, 0.0000, 0.0155],\n",
+       "        [0.4375, 0.1250, 0.0000, 0.1036],\n",
+       "        [0.4375, 0.0000, 0.0000, 0.0157],\n",
+       "        [0.3000, 0.0000, 0.0000, 0.0165],\n",
+       "        [0.6750, 0.0000, 0.0000, 0.1012],\n",
+       "        ...,\n",
+       "        [0.3125, 0.0000, 0.0000, 0.0138],\n",
+       "        [0.4875, 0.0000, 0.8333, 0.0568],\n",
+       "        [0.3375, 0.0000, 0.0000, 0.0254],\n",
+       "        [0.2375, 0.0000, 0.0000, 0.0586],\n",
+       "        [0.3000, 0.1250, 0.3333, 0.0458],\n",
+       "        [0.3250, 0.0000, 0.0000, 0.0586],\n",
+       "        [0.4000, 0.0000, 0.0000, 0.0151]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from torch import tensor\n",
+    "\n",
+    "# And one for the target. Also known as dependent variables or outputs\n",
+    "t_dep = tensor(train_dataset_df.Survived)\n",
+    "\n",
+    "indep_cols = ['Age', 'SibSp', 'Parch', 'Fare']\n",
+    "\n",
+    "# We need to do 2 things before proceeding so that we can use our data.\n",
+    "# 1. Replace all the nans by the mode of that column\n",
+    "for col in indep_cols:\n",
+    "    mode_val = train_dataset_df[col].mode()[0]\n",
+    "    train_dataset_df[col] = train_dataset_df[col].fillna(mode_val)\n",
+    "\n",
+    "# 2. to prevent one column from dominating all the others, by making each row range between 0 and 1. \n",
+    "# We can do this by dividing each entry by\n",
+    "# the max value on that column\n",
+    "for col in indep_cols:\n",
+    "    max_val = train_dataset_df[col].max()\n",
+    "    train_dataset_df[col] = train_dataset_df[col] / max_val\n",
+    "\n",
+    "# Create a tensor with our predictors. Also known as independent variables, features, or inputs.\n",
+    "t_indep = tensor(train_dataset_df[indep_cols].values, dtype=torch.float)\n",
+    "t_indep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks good (?)...\n",
+    "\n",
+    "Because we want to calculate accuracy later, for fun, let's also keep a chunk of the training data for this. This is called a `validation set`.\n",
+    "\n",
+    "Note: This other notebook explains the difference between the `validation` set and the `test` set. They seem similar, but looks like it's important not to confuse these two concepts. https://www.kaggle.com/code/jhoward/getting-started-with-nlp-for-absolute-beginners#Test-and-validation-sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.780061Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.779958Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.784638Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.784367Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(713, 178)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastai.data.transforms import RandomSplitter\n",
+    "trn_split,val_split=RandomSplitter(seed=42)(train_dataset_df)\n",
+    "\n",
+    "train_set_features,validation_set_features = t_indep[trn_split],t_indep[val_split]\n",
+    "train_set_targets,validation_set_targets = t_dep[trn_split],t_dep[val_split]\n",
+    "len(train_set_features),len(validation_set_features)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can generate random weights (`m`s) for each of our features. We're using a linear model, effectively calculating a weighted sum of the features: $f(x) = m_{Age}*x_{Age} + m_{SibSp}*x_{SibSp} + m_{Parch}*x_{Parch} + m_{Fare}*x_{Fare}$. We will adjust these weights to predict passenger survival based on the features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.785990Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.785876Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.789171Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.788924Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 0.3823,  0.4150, -0.1171,  0.4593], requires_grad=True)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def generate_random_coefficients(num_coeffs):\n",
+    "    torch.manual_seed(42)\n",
+    "    coeffs = torch.rand(num_coeffs)-0.5 # pick random numbers in the range (-0.5,0.5)\n",
+    "    return coeffs.requires_grad_()\n",
+    "\n",
+    "nn_coeffs=generate_random_coefficients(num_coeffs=train_set_features.shape[1])\n",
+    "nn_coeffs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.790485Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.790366Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.793251Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.792994Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.return_types.topk(\n",
+       "values=tensor([0.6265, 0.6265, 0.6118], grad_fn=<TopkBackward0>),\n",
+       "indices=tensor([183,  94, 462]))"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def calc_preds(coeffs, features): return (features*coeffs).sum(axis=1)\n",
+    "\n",
+    "predictions = calc_preds(nn_coeffs, train_set_features)\n",
+    "predictions.topk(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.794517Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.794426Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.797184Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.796935Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(0.4199, grad_fn=<MeanBackward0>)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def calc_loss(coeffs, features, targets): return torch.abs(calc_preds(coeffs, features)-targets).mean()\n",
+    "\n",
+    "loss = calc_loss(coeffs=nn_coeffs, features=train_set_features, targets=train_set_targets)\n",
+    "loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.798428Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.798333Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.800781Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.800519Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([ 0.0990,  0.0191,  0.0030, -0.0091])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss.backward()\n",
+    "nn_coeffs.grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.802020Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.801933Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.810756Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.810498Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.420; 0.380; 0.403; 0.473; 0.430; 0.389; 0.427; 0.480; 0.434; 0.391; 0.406; 0.478; 0.431; 0.387; 0.407; 0.470; 0.422; 0.379; 0.427; 0.465; 0.416; 0.372; 0.436; 0.462; 0.411; 0.368; 0.435; 0.458; 0.407; 0.365; 0.418; 0.455; 0.404; 0.364; 0.387; 0.445; 0.394; 0.371; 0.432; 0.384; 0.393; 0.455; 0.401; 0.364; 0.367; 0.427; 0.453; 0.398; 0.366; 0.420; 0.371; 0.415; 0.454; 0.398; 0.367; 0.421; 0.372; 0.413; 0.453; 0.396; "
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'Age': tensor(0.3823),\n",
+       " 'SibSp': tensor(0.4150),\n",
+       " 'Parch': tensor(-0.1171),\n",
+       " 'Fare': tensor(0.4593)}"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def one_epoch(coeffs, lr, train_set_features_set, train_set_targets_set):\n",
+    "    loss = calc_loss(coeffs, train_set_features_set, train_set_targets_set)\n",
+    "    loss.backward()\n",
+    "    with torch.no_grad():\n",
+    "        coeffs.sub_(coeffs.grad * lr)\n",
+    "        coeffs.grad.zero_()\n",
+    "    print(f\"{loss:.3f}\", end=\"; \")\n",
+    "    \n",
+    "def train_model(train_set_features_set, train_set_targets_set, epochs=60, lr=4):\n",
+    "    torch.manual_seed(442)\n",
+    "    coeffs = generate_random_coefficients(num_coeffs=t_indep.shape[1])\n",
+    "    for i in range(epochs): one_epoch(coeffs, lr=lr, train_set_features_set=train_set_features_set, train_set_targets_set=train_set_targets_set)\n",
+    "    return coeffs\n",
+    "\n",
+    "final_weights = train_model(train_set_features, train_set_targets)\n",
+    "\n",
+    "def show_coeffs(coeffs): return dict(zip(indep_cols, coeffs.requires_grad_(False)))\n",
+    "\n",
+    "show_coeffs(nn_coeffs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have weights, let's do predictions then."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.812052Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.811964Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.814746Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.814506Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([0.1258, 0.1216, 0.1212, 0.1519, 0.1311, 0.2332, 0.0116, 0.1454, 0.2672, 0.1671, 0.1600, 0.1607, 0.0614, 0.1217, 0.1683, 0.2388,\n",
+       "        0.3344, 0.1232, 0.2097, 0.1559, 0.1218, 0.2914, 0.3181, 0.3005, 0.1019, 0.1882, 0.0471, 0.3201, 0.0890, 0.1216, 0.0586, 0.3388,\n",
+       "        0.0979, 0.1530, 0.1544, 0.2061, 0.2551, 0.1768, 0.1219, 0.1784, 0.1002, 0.1219, 0.1587, 0.2213, 0.1028, 0.1339, 0.1749, 0.1963,\n",
+       "        0.1209, 0.1116, 0.3947, 0.3054, 0.2396, 0.1258, 0.1337, 0.1623, 0.1216, 0.2378, 0.1076, 0.1506, 0.1682, 0.1394, 0.4092, 0.1075,\n",
+       "        0.2854, 0.1501, 0.1263, 0.2080, 0.2374, 0.2271, 0.1313, 0.0893, 0.1575, 0.1232, 0.1379, 0.0834, 0.1219, 0.3141, 0.1024, 0.2380,\n",
+       "        0.1240, 0.4003, 0.1170, 0.1212, 0.2483, 0.1696, 0.2355, 0.1873, 0.2072, 0.1216, 0.1604, 0.1219, 0.2388, 0.1498, 0.1051, 0.1836,\n",
+       "        0.2431, 0.2393, 0.1121, 0.0968, 0.1254, 0.1216, 0.1600, 0.1504, 0.2511, 0.2123, 0.1519, 0.2585, 0.2530, 0.1968, 0.1419, 0.2161,\n",
+       "        0.1416, 0.2902, 0.1219, 0.4060, 0.2535, 0.1216, 0.2075, 0.4413, 0.3000, 0.3901, 0.1216, 0.2205, 0.1212, 0.1655, 0.1264, 0.1313,\n",
+       "        0.0850, 0.2854, 0.1540, 0.1810, 0.2472, 0.1670, 0.2979, 0.0980, 0.2940, 0.1471, 0.2341, 0.1861, 0.1550, 0.3705, 0.2213, 0.1599,\n",
+       "        0.2736, 0.4228, 0.1893, 0.0957, 0.0923, 0.2085, 0.2059, 0.0826, 0.1098, 0.2523, 0.2393, 0.5530, 0.1219, 0.1563, 0.1741, 0.1277,\n",
+       "        0.1591, 0.2153, 0.1845, 0.2924, 0.2974, 0.3754, 0.1787, 0.2123, 0.5154, 0.2902, 0.1512, 0.1038, 0.2167, 0.1232, 0.2174, 0.1966,\n",
+       "        0.1420, 0.0896])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calc_preds(nn_coeffs, validation_set_features)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's hard not to notice that we should be predicitng a `0` or `1` value, but instead we're getting a lot of negatives. \n",
+    "\n",
+    "For simplicity, let's ignore this and say that everything above `0.5` survived."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.816037Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.815951Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.818294Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.818051Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True count was 2 should have been 72\n",
+      "False count was 176 should have been 106\n"
+     ]
+    }
+   ],
+   "source": [
+    "preds = calc_preds(nn_coeffs, validation_set_features)\n",
+    "\n",
+    "print(f\"True count was {torch.sum(preds>0.5)} should have been {torch.sum(validation_set_targets.bool())}\")\n",
+    "print(f\"False count was {torch.sum(preds<=0.5)} should have been {len( validation_set_targets.bool()) - torch.sum(validation_set_targets.bool())}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With this we can use our `validation set` and calculate the `%` of predicitons that we are getting correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.819584Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.819497Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.822152Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.821904Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor(0.5843)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def calc_accuracy(predictions, validation_set_features, validation_set_targets):\n",
+    "    # Convert predictions to boolean values (True if > 0.5, False otherwise)\n",
+    "    bool_predictions = predictions > 0.5\n",
+    "    # Convert validation dependent variable to boolean values\n",
+    "    bool_validation_set_targets = validation_set_targets.bool()\n",
+    "    # Compare boolean predictions with boolean validation dependent variable to find correct predictions\n",
+    "    correct_predictions = bool_validation_set_targets == bool_predictions\n",
+    "    # Convert correct predictions (boolean) to float (1.0 for True, 0.0 for False)\n",
+    "    accuracy_float = correct_predictions.float()\n",
+    "    # Calculate the mean of the accuracy_float to get the overall accuracy\n",
+    "    accuracy_val = accuracy_float.mean()\n",
+    "    return accuracy_val\n",
+    "\n",
+    "accuracy_result = calc_accuracy(predictions=preds, validation_set_features=validation_set_features, validation_set_targets=validation_set_targets)\n",
+    "print(accuracy_result)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks like we're doing slightly better than throwing a coin. I say this is a success 🤔❓🤔 I don't think so...\n",
+    "\n",
+    "As seen in the counts above (\"True count was 2 should have been 72\", \"False count was 176 should have been 106\"), the model predicts most instances as False (not survived).\n",
+    "It correctly identifies many of the actual 'not survived' cases, contributing to the accuracy, but incorrectly classifies most 'survived' cases as 'not survived'.\n",
+    "This bias results in an accuracy that is better than random (50%), but not very high, around 58%.\n",
+    "\n",
+    "The [Linear model and neural net from scratch](https://www.kaggle.com/code/jhoward/linear-model-and-neural-net-from-scratch#Training-the-linear-model) goes much further on this exercise. It uses other techniques to clean-up and normalize the data, it also uses the non-numerical by transforming them to numericals, and then uses a sigmoid that differently form our linear function, always give a value between `0` and `1`. \n",
+    "\n",
+    "For me, this was enought to get an better overview of what's happening inside a neural network."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3 Do Deep Learning\n",
+    "\n",
+    "In the section above we implemented a simple Neural Network. Now let's explore Deep Learning, which is what truly unlocks the power of Neural Networks.\n",
+    "\n",
+    "Deep Learning involves creating Neural Networks with multiple layers. Instead of a single layer, we stack layers of neurons, allowing the network to learn more complex patterns and representations from the data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.823464Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.823372Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.827841Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.827590Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[tensor([[ 0.3823,  0.4150, -0.1171,  0.4593, -0.1096,  0.1009, -0.2434,  0.2936,  0.4408, -0.3668],\n",
+       "         [ 0.4346,  0.0936,  0.3694,  0.0677,  0.2411, -0.0706,  0.3854,  0.0739, -0.2334,  0.1274],\n",
+       "         [-0.2304, -0.0586, -0.2031,  0.3317, -0.3947, -0.2305, -0.1412, -0.3006,  0.0472, -0.4938],\n",
+       "         [ 0.4516, -0.4247,  0.3860,  0.0832, -0.1624,  0.3090,  0.0779,  0.4040,  0.0547, -0.1577]], requires_grad=True),\n",
+       " tensor([[ 0.1343, -0.1356,  0.2104,  0.4464,  0.2890, -0.2186,  0.2886,  0.0895,  0.2539, -0.3048],\n",
+       "         [-0.4950, -0.1932, -0.3835,  0.4103,  0.1440,  0.2071,  0.1581, -0.0087,  0.3913, -0.3553],\n",
+       "         [ 0.0315, -0.3413,  0.1542, -0.1722,  0.1532, -0.1042,  0.4147, -0.2964, -0.2982, -0.2982],\n",
+       "         [ 0.4497,  0.1666,  0.4811, -0.4126, -0.4959, -0.3912, -0.3363,  0.2025,  0.1790,  0.4155],\n",
+       "         [-0.2582, -0.3409,  0.2653, -0.2021,  0.3035, -0.1187,  0.2860, -0.3885, -0.2523,  0.1524],\n",
+       "         [ 0.1057, -0.1275,  0.2980,  0.3399, -0.3626, -0.2669,  0.4578, -0.1687, -0.1773, -0.4838],\n",
+       "         [-0.2863,  0.1249, -0.0660, -0.3629,  0.0117, -0.3415, -0.4242, -0.2753, -0.4376, -0.3184],\n",
+       "         [ 0.4998,  0.0944,  0.1541, -0.4663, -0.3284, -0.1664,  0.0782, -0.4400, -0.2154, -0.2993],\n",
+       "         [ 0.0014, -0.1861, -0.0346, -0.3388, -0.3432, -0.2917, -0.1711, -0.3946,  0.4192, -0.0992],\n",
+       "         [ 0.4302,  0.1558, -0.4234,  0.3460, -0.1376, -0.1917, -0.4150, -0.4971,  0.1431, -0.1092]], requires_grad=True),\n",
+       " tensor([[ 0.1947],\n",
+       "         [-0.4103],\n",
+       "         [ 0.3712],\n",
+       "         [-0.3670],\n",
+       "         [-0.0863],\n",
+       "         [ 0.1044],\n",
+       "         [ 0.2581],\n",
+       "         [ 0.4037],\n",
+       "         [ 0.4555],\n",
+       "         [-0.3965]], requires_grad=True)]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def generate_random_coefficients_for_deep_learning(n_coeff, num_neurons_per_hidden_layer=[10, 10]):\n",
+    "    torch.manual_seed(42)\n",
+    "    # Define the number of neurons for each layer, including input, hidden, and output layers.\n",
+    "    # The input layer size is n_coeff, hidden layers sizes are from num_neurons_per_hidden_layer, and output layer size is 1.\n",
+    "    num_neurons = [n_coeff] + num_neurons_per_hidden_layer + [1]\n",
+    "    layers = []\n",
+    "    for i in range(len(num_neurons)-1):\n",
+    "        # Determine the size of the input for the current layer from the previous layer's neuron count\n",
+    "        layer_input_size = num_neurons[i]\n",
+    "        # Determine the size of the output for the current layer from the current layer's neuron count\n",
+    "        layer_output_size = num_neurons[i+1]\n",
+    "        # Initialize a layer with random weights between -0.5 and 0.5.\n",
+    "        # torch.rand generates uniform random numbers between 0 and 1, then we shift and scale to get range [-0.5, 0.5].\n",
+    "        # requires_grad_() is set to True to enable gradient tracking for these tensors, which is needed for backpropagation.\n",
+    "        layer = (torch.rand(layer_input_size, layer_output_size)-0.5).requires_grad_()\n",
+    "        layers.append(layer)\n",
+    "    return layers\n",
+    "\n",
+    "dnn_layers_coeffs = generate_random_coefficients_for_deep_learning(n_coeff=train_set_features.shape[1], num_neurons_per_hidden_layer=[10, 10])\n",
+    "dnn_layers_coeffs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can test how we do without any training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.829131Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.829046Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.831925Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.831684Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True count was 12 should have been 72\n",
+      "False count was 166 should have been 106\n"
+     ]
+    }
+   ],
+   "source": [
+    "def calc_preds_for_deep_learning(coeffs, features):\n",
+    "    # @ is matrix multiplication in Python\n",
+    "    # It was introduced in Python 3.5 as part of [PEP 465](https://peps.python.org/pep-0465/)\n",
+    "    layer_features = features\n",
+    "    for layer in coeffs[:-1]:\n",
+    "        layer_features = layer_features @ layer\n",
+    "    layer_features = layer_features @ coeffs[-1]\n",
+    "    return layer_features.squeeze()\n",
+    "\n",
+    "def calc_loss_for_deep_learning(coeffs, features, targets): return torch.abs(calc_preds_for_deep_learning(coeffs, features)-targets).mean()\n",
+    "\n",
+    "dnn_preds = calc_preds_for_deep_learning(coeffs=dnn_layers_coeffs, features=validation_set_features)\n",
+    "\n",
+    "print(f\"True count was {torch.sum(dnn_preds>0.5)} should have been {torch.sum(validation_set_targets.bool())}\")\n",
+    "print(f\"False count was {torch.sum(dnn_preds<=0.5)} should have been {len( validation_set_targets.bool()) - torch.sum(validation_set_targets.bool())}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we need to do the grandient descent for all the coeffs on each layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.833191Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.833100Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.835792Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.835550Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def one_epoch_for_deep_learning(coeffs, lr, train_set_features_set, train_set_targets_set):\n",
+    "    loss = calc_loss_for_deep_learning(coeffs, train_set_features_set, train_set_targets_set)\n",
+    "    loss.backward()\n",
+    "    with torch.no_grad():\n",
+    "        for layer in coeffs:\n",
+    "            layer -= layer.grad * lr\n",
+    "            layer.grad.zero_()\n",
+    "    \n",
+    "def train_model_for_deep_learning(train_set_features_set, train_set_targets_set, num_neurons_per_hidden_layer=[10, 10], epochs=60, lr=4):\n",
+    "    torch.manual_seed(442)\n",
+    "    coeffs = generate_random_coefficients_for_deep_learning(n_coeff=train_set_features_set.shape[1], num_neurons_per_hidden_layer=num_neurons_per_hidden_layer)\n",
+    "    for i in range(epochs): one_epoch_for_deep_learning(coeffs, lr=lr, train_set_features_set=train_set_features_set, train_set_targets_set=train_set_targets_set)\n",
+    "    return coeffs # Returns the trained coefficients, which have the same structure as generate_random_coefficients_for_deep_learning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's test it then with different combinations of hidden layers and neurons per layer..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.837113Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.837024Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.910994Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.910687Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hidden layers: [10, 10]\n",
+      "True count was 0 should have been 72\n",
+      "False count was 0 should have been 106\n",
+      "Accuracy: 0.5955055952072144\n",
+      "--------------------\n",
+      "Hidden layers: [20, 20]\n",
+      "True count was 0 should have been 72\n",
+      "False count was 0 should have been 106\n",
+      "Accuracy: 0.5955055952072144\n",
+      "--------------------\n",
+      "Hidden layers: [5, 5, 5]\n",
+      "True count was 0 should have been 72\n",
+      "False count was 178 should have been 106\n",
+      "Accuracy: 0.5955055952072144\n",
+      "--------------------\n",
+      "Hidden layers: [30]\n",
+      "True count was 0 should have been 72\n",
+      "False count was 178 should have been 106\n",
+      "Accuracy: 0.5955055952072144\n",
+      "--------------------\n",
+      "Hidden layers: []\n",
+      "True count was 5 should have been 72\n",
+      "False count was 173 should have been 106\n",
+      "Accuracy: 0.6123595237731934\n",
+      "--------------------\n",
+      "Hidden layers: [2]\n",
+      "True count was 0 should have been 72\n",
+      "False count was 178 should have been 106\n",
+      "Accuracy: 0.5955055952072144\n",
+      "--------------------\n",
+      "Hidden layers: [50]\n",
+      "True count was 0 should have been 72\n",
+      "False count was 178 should have been 106\n",
+      "Accuracy: 0.5955055952072144\n",
+      "--------------------\n",
+      "Hidden layers: [2, 2]\n",
+      "True count was 0 should have been 72\n",
+      "False count was 178 should have been 106\n",
+      "Accuracy: 0.5955055952072144\n",
+      "--------------------\n",
+      "Hidden layers: [50, 50]\n",
+      "True count was 0 should have been 72\n",
+      "False count was 0 should have been 106\n",
+      "Accuracy: 0.5955055952072144\n",
+      "--------------------\n",
+      "Hidden layers: [5, 10, 5]\n",
+      "True count was 3 should have been 72\n",
+      "False count was 175 should have been 106\n",
+      "Accuracy: 0.601123571395874\n",
+      "--------------------\n",
+      "Hidden layers: [2, 2, 2, 2]\n",
+      "True count was 0 should have been 72\n",
+      "False count was 178 should have been 106\n",
+      "Accuracy: 0.5955055952072144\n",
+      "--------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "for num_neurons in [[10, 10], [20, 20],[5, 5, 5],[30], [], [2], [50], [2, 2], [50, 50], [5, 10, 5], [2, 2, 2, 2]]:\n",
+    "    dnn_final_weights = train_model_for_deep_learning(train_set_features, train_set_targets, num_neurons_per_hidden_layer=num_neurons)\n",
+    "    dnn_preds = calc_preds_for_deep_learning(coeffs=dnn_final_weights, features=validation_set_features)\n",
+    "    accuracy = calc_accuracy(predictions=dnn_preds, validation_set_features=validation_set_features, validation_set_targets=validation_set_targets)\n",
+    "\n",
+    "    print(f\"Hidden layers: {num_neurons}\")\n",
+    "    print(f\"True count was {torch.sum(dnn_preds>0.5)} should have been {torch.sum(validation_set_targets.bool())}\")\n",
+    "    print(f\"False count was {torch.sum(dnn_preds<=0.5)} should have been {len( validation_set_targets.bool()) - torch.sum(validation_set_targets.bool())}\")\n",
+    "    print(f\"Accuracy: {accuracy}\")\n",
+    "    print(\"-\" * 20) # Separator for readability\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Not a lot has changed...\n",
+    "\n",
+    "Just for fun, we can see how adding a sigmoid and ReLU would affect the results... The code is Ctrl+V Ctrl+C from above, but with a `smarter_calc_preds_for_deep_learning`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.912322Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.912232Z",
+     "iopub.status.idle": "2025-03-04T16:43:25.915671Z",
+     "shell.execute_reply": "2025-03-04T16:43:25.915426Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import torch.nn.functional as F\n",
+    "\n",
+    "def smarter_calc_preds_for_deep_learning(coeffs, features):\n",
+    "    # @ is matrix multiplication in Python\n",
+    "    # It was introduced in Python 3.5 as part of [PEP 465](https://peps.python.org/pep-0465/)\n",
+    "    layer_features = features\n",
+    "    for layer in coeffs[:-1]:\n",
+    "        layer_features = F.relu(layer_features @ layer)\n",
+    "    layer_features = layer_features @ coeffs[-1]\n",
+    "    return torch.sigmoid(layer_features.squeeze())\n",
+    "\n",
+    "def smarter_calc_loss_for_deep_learning(coeffs, features, targets):\n",
+    "    predictions = smarter_calc_preds_for_deep_learning(coeffs, features)\n",
+    "    return F.binary_cross_entropy(predictions, targets) # Changed loss to Binary Cross Entropy\n",
+    "\n",
+    "def smarter_one_epoch_for_deep_learning(coeffs, lr, train_set_features_set, train_set_targets_set):\n",
+    "    loss = smarter_calc_loss_for_deep_learning(coeffs, train_set_features_set, train_set_targets_set)\n",
+    "    loss.backward()\n",
+    "    with torch.no_grad():\n",
+    "        for layer in coeffs:\n",
+    "            layer -= layer.grad * lr\n",
+    "            layer.grad.zero_()\n",
+    "    \n",
+    "def smarter_train_model_for_deep_learning(train_set_features_set, train_set_targets_set, num_neurons_per_hidden_layer=[10, 10], epochs=60, lr=4):\n",
+    "    torch.manual_seed(442)\n",
+    "    coeffs = generate_random_coefficients_for_deep_learning(n_coeff=train_set_features_set.shape[1], num_neurons_per_hidden_layer=num_neurons_per_hidden_layer)\n",
+    "    for i in range(epochs): smarter_one_epoch_for_deep_learning(coeffs, lr=lr, train_set_features_set=train_set_features_set, train_set_targets_set=train_set_targets_set)\n",
+    "    return coeffs # Returns the trained coefficients, which have the same structure as generate_random_coefficients_for_deep_learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-03-04T16:43:25.916926Z",
+     "iopub.status.busy": "2025-03-04T16:43:25.916838Z",
+     "iopub.status.idle": "2025-03-04T16:43:26.020998Z",
+     "shell.execute_reply": "2025-03-04T16:43:26.020670Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hidden layers: [10, 10]\n",
+      "True count was 21 should have been 72\n",
+      "False count was 157 should have been 106\n",
+      "Accuracy: 0.6685393452644348\n",
+      "--------------------\n",
+      "Hidden layers: [20, 20]\n",
+      "True count was 60 should have been 72\n",
+      "False count was 118 should have been 106\n",
+      "Accuracy: 0.6966292262077332\n",
+      "--------------------\n",
+      "Hidden layers: [5, 5, 5]\n",
+      "True count was 0 should have been 72\n",
+      "False count was 178 should have been 106\n",
+      "Accuracy: 0.5955055952072144\n",
+      "--------------------\n",
+      "Hidden layers: [30]\n",
+      "True count was 29 should have been 72\n",
+      "False count was 149 should have been 106\n",
+      "Accuracy: 0.6910112500190735\n",
+      "--------------------\n",
+      "Hidden layers: []\n",
+      "True count was 11 should have been 72\n",
+      "False count was 167 should have been 106\n",
+      "Accuracy: 0.6348314881324768\n",
+      "--------------------\n",
+      "Hidden layers: [2]\n",
+      "True count was 0 should have been 72\n",
+      "False count was 178 should have been 106\n",
+      "Accuracy: 0.5955055952072144\n",
+      "--------------------\n",
+      "Hidden layers: [50]\n",
+      "True count was 33 should have been 72\n",
+      "False count was 145 should have been 106\n",
+      "Accuracy: 0.6910112500190735\n",
+      "--------------------\n",
+      "Hidden layers: [2, 2]\n",
+      "True count was 0 should have been 72\n",
+      "False count was 178 should have been 106\n",
+      "Accuracy: 0.5955055952072144\n",
+      "--------------------\n",
+      "Hidden layers: [50, 50]\n",
+      "True count was 32 should have been 72\n",
+      "False count was 146 should have been 106\n",
+      "Accuracy: 0.6853932738304138\n",
+      "--------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hidden layers: [5, 10, 5]\n",
+      "True count was 28 should have been 72\n",
+      "False count was 150 should have been 106\n",
+      "Accuracy: 0.6853932738304138\n",
+      "--------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hidden layers: [2, 2, 2, 2]\n",
+      "True count was 0 should have been 72\n",
+      "False count was 178 should have been 106\n",
+      "Accuracy: 0.5955055952072144\n",
+      "--------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "for num_neurons in [[10, 10], [20, 20],[5, 5, 5],[30], [], [2], [50], [2, 2], [50, 50], [5, 10, 5], [2, 2, 2, 2]]:\n",
+    "    dnn_final_weights = smarter_train_model_for_deep_learning(train_set_features, train_set_targets.float(), num_neurons_per_hidden_layer=num_neurons)\n",
+    "    dnn_preds = smarter_calc_preds_for_deep_learning(coeffs=dnn_final_weights, features=validation_set_features)\n",
+    "    accuracy = calc_accuracy(predictions=dnn_preds, validation_set_features=validation_set_features, validation_set_targets=validation_set_targets)\n",
+    "\n",
+    "    print(f\"Hidden layers: {num_neurons}\")\n",
+    "    print(f\"True count was {torch.sum(dnn_preds>0.5)} should have been {torch.sum(validation_set_targets.bool())}\")\n",
+    "    print(f\"False count was {torch.sum(dnn_preds<=0.5)} should have been {len( validation_set_targets.bool()) - torch.sum(validation_set_targets.bool())}\")\n",
+    "    print(f\"Accuracy: {accuracy}\")\n",
+    "    print(\"-\" * 20) # Separator for readability"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Interesting. That definitely improved.\n",
+    "\n",
+    "I will stop here for now. However, the next step will likely be to add the boolean variables like `is_male`, `is_female`, `is_class_1`, etc. If I understood that correctly and I'm not making any mistakes, it should bring me to around 80% accuracy, like we see on the fast.ai notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {
+     "00a3879a01cf4b839d14e6414f01465c": {
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+       "_model_module_version": "2.0.0",
+       "_model_name": "SliderStyleModel",
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@@ -12833,7 +14843,60 @@
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@@ -12886,39 +14949,7 @@
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-     "775fd653cd0b4ce8a7c28d4558c8c4e0": {
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@@ -12934,7 +14965,7 @@
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-     "78dc7d55d786467eabaac72e2a0d26a8": {
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@@ -12987,7 +15018,36 @@
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-     "7d036ed0a14645338b9208a3dc0be7f1": {
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+       "_model_module": "@jupyter-widgets/output",
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+       "_view_module": "@jupyter-widgets/output",
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+       "_view_name": "OutputView",
+       "layout": "IPY_MODEL_a2884731c1f742f1b47a623936e36921",
+       "msg_id": "",
+       "outputs": [
+        {
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",
+          "text/plain": "<Figure size 576x432 with 1 Axes>"
+         },
+         "metadata": {},
+         "output_type": "display_data"
+        }
+       ],
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "a2884731c1f742f1b47a623936e36921": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -13040,23 +15100,67 @@
        "width": null
       }
      },
-     "8019035d4ebb4a59bb545e6a98950089": {
+     "a9e475e3ee8d463999c6188b340a0101": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "SliderStyleModel",
+      "model_name": "FloatSliderModel",
       "state": {
+       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "SliderStyleModel",
+       "_model_name": "FloatSliderModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
+       "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "StyleView",
-       "description_width": "",
-       "handle_color": null
+       "_view_name": "FloatSliderView",
+       "behavior": "drag-tap",
+       "continuous_update": true,
+       "description": "b1",
+       "description_allow_html": false,
+       "disabled": false,
+       "layout": "IPY_MODEL_3be97997f81547c481beabd4aa957bbf",
+       "max": 1.5,
+       "min": -4.5,
+       "orientation": "horizontal",
+       "readout": true,
+       "readout_format": ".2f",
+       "step": 0.1,
+       "style": "IPY_MODEL_efb8d42b15114d21be450e5ddefc3fb1",
+       "tabbable": null,
+       "tooltip": null,
+       "value": -1.5
+      }
+     },
+     "b21a949cea47403ba887fa7296930317": {
+      "model_module": "@jupyter-widgets/output",
+      "model_module_version": "1.0.0",
+      "model_name": "OutputModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/output",
+       "_model_module_version": "1.0.0",
+       "_model_name": "OutputModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/output",
+       "_view_module_version": "1.0.0",
+       "_view_name": "OutputView",
+       "layout": "IPY_MODEL_b40e4ed954334c728b658a5b5d650fdd",
+       "msg_id": "",
+       "outputs": [
+        {
+         "data": {
+          "image/png": 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",
+          "text/plain": "<Figure size 576x432 with 1 Axes>"
+         },
+         "metadata": {},
+         "output_type": "display_data"
+        }
+       ],
+       "tabbable": null,
+       "tooltip": null
       }
      },
-     "82737cd9ba204f51a600fa48bb2ddfb6": {
+     "b40e4ed954334c728b658a5b5d650fdd": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -13109,7 +15213,7 @@
        "width": null
       }
      },
-     "84e18e2e2b8942c6a6a3ca5f5e6a65b7": {
+     "b47b2de5b29a461c82d575fc28b813f0": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "FloatSliderModel",
@@ -13124,23 +15228,81 @@
        "_view_name": "FloatSliderView",
        "behavior": "drag-tap",
        "continuous_update": true,
-       "description": "a",
+       "description": "c",
+       "description_allow_html": false,
+       "disabled": false,
+       "layout": "IPY_MODEL_96ff80dc23ef4cf991a91a300b18b3f7",
+       "max": 4.5,
+       "min": -1.5,
+       "orientation": "horizontal",
+       "readout": true,
+       "readout_format": ".2f",
+       "step": 0.1,
+       "style": "IPY_MODEL_c0e920f8ed0641a385f1d6e28309631b",
+       "tabbable": null,
+       "tooltip": null,
+       "value": 1.5
+      }
+     },
+     "baf5304da5374d4a92871ae0d0974507": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "VBoxModel",
+      "state": {
+       "_dom_classes": [
+        "widget-interact"
+       ],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "VBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "VBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_1c1eeb75a96a49cb80e1c216b8b1f23e",
+        "IPY_MODEL_059ce485b9ef4c00874226a7cb3d463d",
+        "IPY_MODEL_bdb445d364d143b192fa6345dc6d8173",
+        "IPY_MODEL_1f776ef9cba94de4a260dae3ce3ee0a3"
+       ],
+       "layout": "IPY_MODEL_fe3f38ba06714945bac6d680698d4293",
+       "tabbable": null,
+       "tooltip": null
+      }
+     },
+     "bdb445d364d143b192fa6345dc6d8173": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "FloatSliderModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "FloatSliderModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "2.0.0",
+       "_view_name": "FloatSliderView",
+       "behavior": "drag-tap",
+       "continuous_update": true,
+       "description": "c",
        "description_allow_html": false,
        "disabled": false,
-       "layout": "IPY_MODEL_7d036ed0a14645338b9208a3dc0be7f1",
+       "layout": "IPY_MODEL_cf5063e76c0a479db7dd401eeea75f64",
        "max": 4.5,
        "min": -1.5,
        "orientation": "horizontal",
        "readout": true,
        "readout_format": ".2f",
        "step": 0.1,
-       "style": "IPY_MODEL_8019035d4ebb4a59bb545e6a98950089",
+       "style": "IPY_MODEL_8312e0e75d2d4b178e676c2090522459",
        "tabbable": null,
        "tooltip": null,
        "value": 1.5
       }
      },
-     "8d8a1abc598b4327afe192da75d63366": {
+     "be31fdb8911044818442c542f55519af": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "SliderStyleModel",
@@ -13156,34 +15318,23 @@
        "handle_color": null
       }
      },
-     "8eb3c197199c48efb68b11c5bfa516f2": {
+     "c0e920f8ed0641a385f1d6e28309631b": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "VBoxModel",
+      "model_name": "SliderStyleModel",
       "state": {
-       "_dom_classes": [
-        "widget-interact"
-       ],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "VBoxModel",
+       "_model_name": "SliderStyleModel",
        "_view_count": null,
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-        "IPY_MODEL_4da90bb8fd4849379fac0377c77819d8",
-        "IPY_MODEL_b0964a7d50714a198510f168f88398e4",
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-       "layout": "IPY_MODEL_b5a88080f09e4567a5c1a70625f915a0",
-       "tabbable": null,
-       "tooltip": null
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-     "9105f5d6d0384002a8b634fb525d066b": {
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@@ -13236,7 +15387,7 @@
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-     "959e96bc35634e66a6ece3f9a44df45b": {
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       "model_name": "LayoutModel",
@@ -13264,92 +15415,32 @@
        "grid_auto_flow": null,
        "grid_auto_rows": null,
        "grid_column": null,
-       "grid_gap": null,
-       "grid_row": null,
-       "grid_template_areas": null,
-       "grid_template_columns": null,
-       "grid_template_rows": null,
-       "height": null,
-       "justify_content": null,
-       "justify_items": null,
-       "left": null,
-       "margin": null,
-       "max_height": null,
-       "max_width": null,
-       "min_height": null,
-       "min_width": null,
-       "object_fit": null,
-       "object_position": null,
-       "order": null,
-       "overflow": null,
-       "padding": null,
-       "right": null,
-       "top": null,
-       "visibility": null,
-       "width": null
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-     "9c41cd0b666646e497dc9838a624d8cb": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "FloatSliderModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "FloatSliderModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "FloatSliderView",
-       "behavior": "drag-tap",
-       "continuous_update": true,
-       "description": "c",
-       "description_allow_html": false,
-       "disabled": false,
-       "layout": "IPY_MODEL_acb1afd73a56437c89e633fc61e9431b",
-       "max": 4.5,
-       "min": -1.5,
-       "orientation": "horizontal",
-       "readout": true,
-       "readout_format": ".2f",
-       "step": 0.1,
-       "style": "IPY_MODEL_4943b42673ca4d5baea56d10ba6cabbd",
-       "tabbable": null,
-       "tooltip": null,
-       "value": 1.5
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-     },
-     "9c49f31d03334fa0b8529f8e98eb4632": {
-      "model_module": "@jupyter-widgets/output",
-      "model_module_version": "1.0.0",
-      "model_name": "OutputModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/output",
-       "_model_module_version": "1.0.0",
-       "_model_name": "OutputModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/output",
-       "_view_module_version": "1.0.0",
-       "_view_name": "OutputView",
-       "layout": "IPY_MODEL_5363794973aa483184316bfc3812741d",
-       "msg_id": "",
-       "outputs": [
-        {
-         "data": {
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",
-          "text/plain": "<Figure size 576x432 with 1 Axes>"
-         },
-         "metadata": {},
-         "output_type": "display_data"
-        }
-       ],
-       "tabbable": null,
-       "tooltip": null
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
       }
      },
-     "9f4cf88d197948dcadd60327606f240e": {
+     "d1fdb660e0fa43c2bdebc3ee3428015b": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
       "model_name": "SliderStyleModel",
@@ -13365,34 +15456,7 @@
        "handle_color": null
       }
      },
-     "a968c18476a64679865d4e155b830426": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "2.0.0",
-      "model_name": "VBoxModel",
-      "state": {
-       "_dom_classes": [
-        "widget-interact"
-       ],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "VBoxModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "VBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_e6aa9caefb08414ca8c0507dccf80f7d",
-        "IPY_MODEL_e3adadcfdd9445eeb68fdece9ba2cf00",
-        "IPY_MODEL_9c41cd0b666646e497dc9838a624d8cb",
-        "IPY_MODEL_b065425609cc4b92a4f1662c71212374"
-       ],
-       "layout": "IPY_MODEL_78dc7d55d786467eabaac72e2a0d26a8",
-       "tabbable": null,
-       "tooltip": null
-      }
-     },
-     "acb1afd73a56437c89e633fc61e9431b": {
+     "d26c2eb870384f8abfb119d7b186ee0f": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "2.0.0",
       "model_name": "LayoutModel",
@@ -13445,96 +15509,35 @@
        "width": null
       }
      },
-     "b065425609cc4b92a4f1662c71212374": {
-      "model_module": "@jupyter-widgets/output",
-      "model_module_version": "1.0.0",
-      "model_name": "OutputModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/output",
-       "_model_module_version": "1.0.0",
-       "_model_name": "OutputModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/output",
-       "_view_module_version": "1.0.0",
-       "_view_name": "OutputView",
-       "layout": "IPY_MODEL_959e96bc35634e66a6ece3f9a44df45b",
-       "msg_id": "",
-       "outputs": [
-        {
-         "data": {
-          "image/png": 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",
-          "text/plain": "<Figure size 576x432 with 1 Axes>"
-         },
-         "metadata": {},
-         "output_type": "display_data"
-        }
-       ],
-       "tabbable": null,
-       "tooltip": null
-      }
-     },
-     "b0964a7d50714a198510f168f88398e4": {
+     "daa10b2c84ad4a9e8384392e574fe11f": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "2.0.0",
-      "model_name": "FloatSliderModel",
+      "model_name": "VBoxModel",
       "state": {
-       "_dom_classes": [],
+       "_dom_classes": [
+        "widget-interact"
+       ],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "2.0.0",
-       "_model_name": "FloatSliderModel",
+       "_model_name": "VBoxModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "2.0.0",
-       "_view_name": "FloatSliderView",
-       "behavior": "drag-tap",
-       "continuous_update": true,
-       "description": "c",
-       "description_allow_html": false,
-       "disabled": false,
-       "layout": "IPY_MODEL_4a3b2bcee39047c48fbc7e6cbf4c158b",
-       "max": 4.5,
-       "min": -1.5,
-       "orientation": "horizontal",
-       "readout": true,
-       "readout_format": ".2f",
-       "step": 0.1,
-       "style": "IPY_MODEL_5a37c1c2ef704e0b884ac990c93f820b",
-       "tabbable": null,
-       "tooltip": null,
-       "value": 1.5
-      }
-     },
-     "b3ee6d94ec9c4cb8857345049339bda8": {
-      "model_module": "@jupyter-widgets/output",
-      "model_module_version": "1.0.0",
-      "model_name": "OutputModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/output",
-       "_model_module_version": "1.0.0",
-       "_model_name": "OutputModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/output",
-       "_view_module_version": "1.0.0",
-       "_view_name": "OutputView",
-       "layout": "IPY_MODEL_9105f5d6d0384002a8b634fb525d066b",
-       "msg_id": "",
-       "outputs": [
-        {
-         "data": {
-          "image/png": 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",
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-         },
-         "metadata": {},
-         "output_type": "display_data"
-        }
+       "_view_name": "VBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_84082c1f0edd47f2a2f9c0cb17e927c2",
+        "IPY_MODEL_a9e475e3ee8d463999c6188b340a0101",
+        "IPY_MODEL_ed68bb8deaa04b3abbd16543f9991bd7",
+        "IPY_MODEL_02d47e4710b5402580f61b3cf76b5c39",
+        "IPY_MODEL_b21a949cea47403ba887fa7296930317"
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-     "b5a88080f09e4567a5c1a70625f915a0": {
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@@ -13587,7 +15590,39 @@
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-     "cd15ca9edbe44dbb8adda3bcf9176ad9": {
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+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SliderStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "SliderStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "description_width": "",
+       "handle_color": null
+      }
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+     "e9f9d4af21014222a95565b91b2d2d90": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "2.0.0",
+      "model_name": "SliderStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "2.0.0",
+       "_model_name": "SliderStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "2.0.0",
+       "_view_name": "StyleView",
+       "description_width": "",
+       "handle_color": null
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+     "ed68bb8deaa04b3abbd16543f9991bd7": {
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@@ -13602,50 +15637,23 @@
        "_view_name": "FloatSliderView",
        "behavior": "drag-tap",
        "continuous_update": true,
-       "description": "a",
+       "description": "m2",
        "description_allow_html": false,
        "disabled": false,
-       "layout": "IPY_MODEL_e23536e4289a428a9946cb40b9505c8a",
+       "layout": "IPY_MODEL_0acf8bb6ecf740b490d80eb8da02effe",
        "max": 4.5,
        "min": -1.5,
        "orientation": "horizontal",
        "readout": true,
        "readout_format": ".2f",
        "step": 0.1,
-       "style": "IPY_MODEL_718047d0695c40d8afd2c76bd5af996d",
+       "style": "IPY_MODEL_750bfc8bee6c462bbff3d04ba0dfa913",
        "tabbable": null,
        "tooltip": null,
        "value": 1.5
       }
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-     "cffd9415d34e4a65a8786ec9d2db966e": {
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-      "model_module_version": "2.0.0",
-      "model_name": "VBoxModel",
-      "state": {
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-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "2.0.0",
-       "_model_name": "VBoxModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "2.0.0",
-       "_view_name": "VBoxView",
-       "box_style": "",
-       "children": [
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-        "IPY_MODEL_d89195c3eddc435cac852b47bda65fb7",
-        "IPY_MODEL_4e135376cb9d496188c9b23261f95411",
-        "IPY_MODEL_b3ee6d94ec9c4cb8857345049339bda8"
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-       "layout": "IPY_MODEL_647eb74c92aa4b8c83f563bcb490e273",
-       "tabbable": null,
-       "tooltip": null
-      }
-     },
-     "d762d858ac654c37b29824de90a01f9a": {
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       "model_name": "SliderStyleModel",
@@ -13661,38 +15669,34 @@
        "handle_color": null
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