From cb9630b78acd93c2735c93a6acf7c88a632afbad Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lo=C3=AFc=20GUEZO?= <loicguezo@gmail.com>
Date: Tue, 6 May 2025 22:23:16 +0200
Subject: [PATCH] feat: add first text page (might change soon)

---
 nnetwork.ipynb | 123 ++++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 116 insertions(+), 7 deletions(-)

diff --git a/nnetwork.ipynb b/nnetwork.ipynb
index cf4e1f4..af3040a 100644
--- a/nnetwork.ipynb
+++ b/nnetwork.ipynb
@@ -5,23 +5,115 @@
    "id": "9b3f1635",
    "metadata": {},
    "source": [
-    "# Neural Network\n",
+    "# Neural Network"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "478651c8",
+   "metadata": {},
+   "source": [
+    "## What is a *Neuron* (artifical)\n",
     "\n",
-    "## What is a neuron in a neural network?"
+    "First of all, **I'm not an Neurologist so i might say some nonsense, i only researched online**. \n",
+    "\n",
+    "An artifical *neuron* works similary to a biological *neuron* in the way it process information. In a brain, like yours, a *neuron* receives signals from other *neurons*, processes them and sends an *output*.\n",
+    "\n",
+    "An artifical *neuron* takes **multiple *inputs*** (such as numbers), applies updated values called **weights** to each *inputs*, adds a constant called **bias**, apply a specific function to normalize the value called **Activation function**, and then `returns` the *output* of the Activation function (such as: **sigmoid**, **ReLU**, etc...)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "126bc01c",
+   "execution_count": null,
+   "id": "7d9d6072",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "# Neuron 1\n",
+    "class Neuron:\n",
+    "    def __init__(self, input_size: int) -> None:\n",
+    "        self.input_size = input_size\n",
+    "        self.weight = [random.uniform(0, 1) for _ in range(self.input_size)]\n",
+    "        self.bias = random.uniform(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1aff9ee6",
+   "metadata": {},
+   "source": [
+    "# 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ca39a42",
    "metadata": {},
    "outputs": [],
    "source": [
     "import math\n",
     "import random\n",
     "\n",
-    "def sigmoid(x: float) -> float:\n",
-    "    return 1/(1 + math.exp(-x))\n",
+    "# Neuron 2\n",
+    "class Neuron:\n",
+    "    def __init__(self, input_size: int) -> None:\n",
+    "        self.input_size = input_size\n",
+    "        self.weight = [random.uniform(0, 1) for _ in range(self.input_size)]\n",
+    "        self.bias = random.uniform(0, 1)\n",
+    "\n",
+    "    def sigmoid(x: float) -> float:\n",
+    "        return 1/(1 + math.exp(-x))\n",
+    "    \n",
+    "    def forward(self, inputs: list) -> float:\n",
+    "        assert len(inputs) == self.input_size, \"error: misnumber inputs number\"\n",
+    "        total = sum(self.weight[i] * inputs[i] for i in range(self.isize)) + self.bias\n",
+    "        return self.sigmoid(total)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6709c5c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neuron output : 0.9001175686881125\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 8 for 8 bits (1 Byte)\n",
+    "nbits: int = 8\n",
+    "neuron = Neuron(nbits)\n",
+    "inputs: list = [1, 0, 1, 0, 0, 1, 1, 0] \n",
+    "\n",
+    "output = neuron.forward(inputs)\n",
+    "print(\"Neuron output :\", output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa57ae8e",
+   "metadata": {},
+   "source": [
+    "# 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6de25ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "import random\n",
     "\n",
     "class Neuron:\n",
     "    def __init__(self, isize: int) -> None:\n",
@@ -32,7 +124,24 @@
     "    def forward(self, inputs: list) -> float:\n",
     "        assert len(inputs) == self.isize, \"error: incorrect inputs number\"\n",
     "        total = sum(self.weight[i] * inputs[i] for i in range(self.isize)) + self.bias\n",
-    "        return sigmoid(total)"
+    "        return self.sigmoid(total)\n",
+    "    \n",
+    "    def sigmoid(x: float) -> float:\n",
+    "        return 1/(1 + math.exp(-x))\n",
+    "\n",
+    "    # target needs to be between 0 and 1\n",
+    "    def train(self, inputs: list, target: float, learning_rate: float = 0.1):\n",
+    "        z = sum(self.weight[i] * inputs[i] for i in range(self.isize)) + self.bias\n",
+    "        output = self.sigmoid(z)\n",
+    "\n",
+    "        error = output - target\n",
+    "        d_sigmoid = output * (1 - output)\n",
+    "        dz = error * d_sigmoid\n",
+    "\n",
+    "        for i in range(self.isize):\n",
+    "            self.weight[i] -= learning_rate * dz * inputs[i]\n",
+    "\n",
+    "        self.bias -= learning_rate * dz\n"
    ]
   }
  ],