0002-.patch

From 01c579bb532ad5d52d4d5d77d87f9ef6bd299e9f Mon Sep 17 00:00:00 2001
From: "Adnan Amin, Ph.D." <geoamins@gmail.com>
Date: Sun, 8 Mar 2026 13:36:32 +0500
Subject: [PATCH 2/2] ...

---
 .../2featurs_logistic_regression_GD.ipynb     |  25 ++-
 .../step_by_step_logistic regression.ipynb    | 144 ++++++++++++++++++
 2 files changed, 163 insertions(+), 6 deletions(-)

diff --git a/ML Week 5.1/2featurs_logistic_regression_GD.ipynb b/ML Week 5.1/2featurs_logistic_regression_GD.ipynb
index d64b1e2..218a168 100644
--- a/ML Week 5.1/2featurs_logistic_regression_GD.ipynb	
+++ b/ML Week 5.1/2featurs_logistic_regression_GD.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "9675b8d1",
    "metadata": {},
    "outputs": [],
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "d9df748a",
    "metadata": {},
    "outputs": [],
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "b12c8bdd",
    "metadata": {},
    "outputs": [],
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "496b0174",
    "metadata": {},
    "outputs": [],
@@ -80,10 +80,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "3a45614b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final Weights:\n",
+      " [[0.66763282]\n",
+      " [0.66763282]]\n",
+      "Final Bias:\n",
+      " -4.395402776214578\n",
+      "Final Loss: 0.21133419669571119\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"Final Weights:\\n\", w)\n",
     "print(\"Final Bias:\\n\", b)\n",
diff --git a/ML Week 5.1/step_by_step_logistic regression.ipynb b/ML Week 5.1/step_by_step_logistic regression.ipynb
index e69de29..63a5497 100644
--- a/ML Week 5.1/step_by_step_logistic regression.ipynb	
+++ b/ML Week 5.1/step_by_step_logistic regression.ipynb	
@@ -0,0 +1,144 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "509e31f9",
+   "metadata": {},
+   "source": [
+    "### Logistic Regression From Scratch (1 Feature)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c82bf0e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Dataset\n",
+    "X = np.array([1, 2, 3]).reshape(-1, 1)   # feature\n",
+    "y = np.array([0, 0, 1]).reshape(-1, 1)   # labels\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "4af339cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize parameters\n",
+    "w = np.zeros((1, 1))  # w=[0​]\n",
+    "b = 0\n",
+    "alpha = 0.1\n",
+    "n = len(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "65b667f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sigmoid function\n",
+    "\n",
+    "def sigmoid(z):\n",
+    "    return 1 / (1 + np.exp(-z))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a8a85443",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1\n",
+      "w = 0.000000, b = -0.016667\n",
+      "Loss = 0.693147\n",
+      "-------------------------\n",
+      "Iteration 2\n",
+      "w = 0.000833, b = -0.032917\n",
+      "Loss = 0.690404\n",
+      "-------------------------\n",
+      "Iteration 3\n",
+      "w = 0.002382, b = -0.048802\n",
+      "Loss = 0.687783\n",
+      "-------------------------\n",
+      "Iteration 4\n",
+      "w = 0.004544, b = -0.064368\n",
+      "Loss = 0.685256\n",
+      "-------------------------\n",
+      "Iteration 5\n",
+      "w = 0.007231, b = -0.079653\n",
+      "Loss = 0.682803\n",
+      "-------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Training loop\n",
+    "\n",
+    "for i in range(5):\n",
+    "    \n",
+    "    # Forward pass\n",
+    "    z = np.dot(X, w) + b\n",
+    "    y_hat = sigmoid(z)\n",
+    "    \n",
+    "    # Compute loss (Binary Cross Entropy)\n",
+    "    loss = -(1/n) * np.sum(\n",
+    "        y * np.log(y_hat) + (1 - y) * np.log(1 - y_hat)\n",
+    "    )\n",
+    "    \n",
+    "    # Compute gradients\n",
+    "    dw = (1/n) * np.dot(X.T, (y_hat - y))\n",
+    "    db = (1/n) * np.sum(y_hat - y)\n",
+    "    \n",
+    "    # Update parameters\n",
+    "    w = w - alpha * dw\n",
+    "    b = b - alpha * db\n",
+    "    \n",
+    "    # Print results\n",
+    "    print(f\"Iteration {i+1}\")\n",
+    "    print(f\"w = {w.flatten()[0]:.6f}, b = {b:.6f}\")\n",
+    "    print(f\"Loss = {loss:.6f}\")\n",
+    "    print(\"-------------------------\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef0566a8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
-- 
2.51.0.windows.1