feat: add ipynb file and write neuron function class

nnetwork.ipynb

@@ -0,0 +1,60 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9b3f1635",
+   "metadata": {},
+   "source": [
+    "# Neural Network\n",
+    "\n",
+    "## What is a neuron in a neural network?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "126bc01c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "import random\n",
+    "\n",
+    "def sigmoid(x: float) -> float:\n",
+    "    return 1/(1 + math.exp(-x))\n",
+    "\n",
+    "class Neuron:\n",
+    "    def __init__(self, isize: int) -> None:\n",
+    "        self.isize = isize\n",
+    "        self.weight = [random.uniform(0, 1) for _ in range(self.isize)]\n",
+    "        self.bias = random.uniform(0, 1)\n",
+    "\n",
+    "    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)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}