microsoft · leestott · Aug 28, 2025 · Aug 28, 2025
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diff --git a/translations/ar/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/translations/ar/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb
diff --git a/translations/ar/lessons/3-NeuralNetworks/03-Perceptron/lab/PerceptronMultiClass.ipynb b/translations/ar/lessons/3-NeuralNetworks/03-Perceptron/lab/PerceptronMultiClass.ipynb
diff --git a/translations/ar/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb b/translations/ar/lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb
diff --git a/translations/ar/lessons/3-NeuralNetworks/04-OwnFramework/lab/MyFW_MNIST.ipynb b/translations/ar/lessons/3-NeuralNetworks/04-OwnFramework/lab/MyFW_MNIST.ipynb
@@ -0,0 +1,183 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# تصنيف أرقام MNIST باستخدام إطار العمل الخاص بنا\n",
+    "\n",
+    "مهمة مختبر من [منهج الذكاء الاصطناعي للمبتدئين](https://github.com/microsoft/ai-for-beginners).\n",
+    "\n",
+    "### قراءة مجموعة البيانات\n",
+    "\n",
+    "يقوم هذا الكود بتنزيل مجموعة البيانات من المستودع على الإنترنت. يمكنك أيضًا نسخ مجموعة البيانات يدويًا من دليل `/data` في مستودع منهج الذكاء الاصطناعي.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+      "\n",
+      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
+      "100  9.9M  100  9.9M    0     0   9.9M      0  0:00:01 --:--:--  0:00:01 15.8M\n"
+     ]
+    }
+   ],
+   "source": [
+    "!rm *.pkl\n",
+    "!wget https://raw.githubusercontent.com/microsoft/AI-For-Beginners/main/data/mnist.pkl.gz\n",
+    "!gzip -d mnist.pkl.gz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "with open('mnist.pkl','rb') as f:\n",
+    "    MNIST = pickle.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = MNIST['Train']['Labels']\n",
+    "data = MNIST['Train']['Features']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "لنرى ما هو شكل البيانات التي لدينا:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(42000, 784)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### تقسيم البيانات\n",
+    "\n",
+    "سنستخدم مكتبة Scikit Learn لتقسيم البيانات بين مجموعة التدريب ومجموعة الاختبار:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train samples: 33600, test samples: 8400\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "features_train, features_test, labels_train, labels_test = train_test_split(data,labels,test_size=0.2)\n",
+    "\n",
+    "print(f\"Train samples: {len(features_train)}, test samples: {len(features_test)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### التعليمات\n",
+    "\n",
+    "1. خذ كود الإطار من الدرس والصقه في هذا الدفتر، أو (من الأفضل) في وحدة Python منفصلة.\n",
+    "1. قم بتعريف وتدريب perceptron ذو طبقة واحدة، مع مراقبة دقة التدريب والتحقق أثناء التدريب.\n",
+    "1. حاول فهم ما إذا كان هناك إفراط في التعميم، وقم بتعديل معلمات الطبقة لتحسين الدقة.\n",
+    "1. كرر الخطوات السابقة لـ perceptron ذو طبقتين وثلاث طبقات. حاول تجربة وظائف تنشيط مختلفة بين الطبقات.\n",
+    "1. حاول الإجابة على الأسئلة التالية:\n",
+    "    - هل تؤثر وظيفة التنشيط بين الطبقات على أداء الشبكة؟\n",
+    "    - هل نحتاج إلى شبكة ذات طبقتين أو ثلاث طبقات لهذه المهمة؟\n",
+    "    - هل واجهت أي مشاكل أثناء تدريب الشبكة؟ خاصة مع زيادة عدد الطبقات.\n",
+    "    - كيف تتصرف أوزان الشبكة أثناء التدريب؟ يمكنك رسم القيمة المطلقة القصوى للأوزان مقابل العصور لفهم العلاقة.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**إخلاء المسؤولية**:  \nتم ترجمة هذا المستند باستخدام خدمة الترجمة بالذكاء الاصطناعي [Co-op Translator](https://github.com/Azure/co-op-translator). بينما نسعى لتحقيق الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو معلومات غير دقيقة. يجب اعتبار المستند الأصلي بلغته الأصلية المصدر الموثوق. للحصول على معلومات حاسمة، يُوصى بالاستعانة بترجمة بشرية احترافية. نحن غير مسؤولين عن أي سوء فهم أو تفسيرات خاطئة تنشأ عن استخدام هذه الترجمة.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.4 64-bit (conda)",
+   "metadata": {
+    "interpreter": {
+     "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+    }
+   },
+   "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": "3.9.5"
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "6fa055f484eb5d6bdf41166a356d3abf",
+   "translation_date": "2025-08-28T03:45:31+00:00",
+   "source_file": "lessons/3-NeuralNetworks/04-OwnFramework/lab/MyFW_MNIST.ipynb",
+   "language_code": "ar"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/translations/ar/lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb b/translations/ar/lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb