Update tu_dataset.json

tu_dataset.json

@@ -1,7 +1,4 @@
 [
-  // =================================================================
-  // 🧠 1. INTELIGENCIA ARTIFICIAL: MANEJO DE DATOS Y SKLEARN BÁSICO
-  // =================================================================
   {
     "prompt": "Función en Python para dividir un dataset (X, y) en conjuntos de entrenamiento y prueba usando Scikit-learn (test_size=0.3):",
     "completion": "from sklearn.model_selection import train_test_split\n\ndef dividir_datos(X, y):\n    X_train, X_test, y_train, y_test = train_test_split(\n        X, y, test_size=0.3, random_state=42\n    )\n    return X_train, X_test, y_train, y_test\n"
@@ -19,9 +16,6 @@
     "completion": "import pandas as pd\n\ndf = pd.read_csv('datos_ia.csv')\nprint(df.head())\n"
   },
   
-  // =================================================================
-  // 🧠 2. DEEP LEARNING: PYTORCH (REDES NEURONALES)
-  // =================================================================
   {
     "prompt": "Clase en PyTorch que define un módulo de Convolución (Conv2d) seguido de una función de activación ReLU:",
     "completion": "import torch.nn as nn\n\nclass BloqueConv(nn.Module):\n    def __init__(self, in_c, out_c):\n        super(BloqueConv, self).__init__()\n        self.conv = nn.Conv2d(in_c, out_c, kernel_size=3, padding=1)\n        self.relu = nn.ReLU()\n\n    def forward(self, x):\n        return self.relu(self.conv(x))\n"
@@ -31,13 +25,10 @@
     "completion": "import torch.nn.init as init\n\ndef inicializar_pesos(m):\n    if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d):\n        init.xavier_uniform_(m.weight.data)\n        if m.bias is not None:\n            init.constant_(m.bias.data, 0)\n"
   },
   {
-    "prompt": "# Configuración del ciclo de entrenamiento PyTorch (Optimizer y Loss Function):\n",
+    "prompt": "Configuración del ciclo de entrenamiento PyTorch (Optimizer y Loss Function):",
     "completion": "import torch.optim as optim\nimport torch.nn as nn\n\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001)\n"
   },
 
-  // =================================================================
-  // 🧠 3. PRE-PROCESAMIENTO AVANZADO Y VISUALIZACIÓN
-  // =================================================================
   {
     "prompt": "Código Python/Matplotlib para mostrar la distribución de una característica usando un histograma:",
     "completion": "import matplotlib.pyplot as plt\n\ndef mostrar_histograma(data, titulo):\n    plt.figure(figsize=(8, 6))\n    plt.hist(data, bins=30, alpha=0.7, color='skyblue', edgecolor='black')\n    plt.title(titulo)\n    plt.xlabel('Valor')\n    plt.ylabel('Frecuencia')\n    plt.grid(axis='y', alpha=0.5)\n    plt.show()\n"