diff --git a/your-code/challenge-1.ipynb b/your-code/challenge-1.ipynb
index 2487c5f..03b25d8 100644
--- a/your-code/challenge-1.ipynb
+++ b/your-code/challenge-1.ipynb
@@ -32,13 +32,219 @@
     "1. Normalize the input data."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here\n",
+    "import pandas as pd\n",
+    "tic_tac_toe = pd.read_csv('tic-tac-toe.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 958 entries, 0 to 957\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column  Non-Null Count  Dtype \n",
+      "---  ------  --------------  ----- \n",
+      " 0   TL      958 non-null    object\n",
+      " 1   TM      958 non-null    object\n",
+      " 2   TR      958 non-null    object\n",
+      " 3   ML      958 non-null    object\n",
+      " 4   MM      958 non-null    object\n",
+      " 5   MR      958 non-null    object\n",
+      " 6   BL      958 non-null    object\n",
+      " 7   BM      958 non-null    object\n",
+      " 8   BR      958 non-null    object\n",
+      " 9   class   958 non-null    bool  \n",
+      "dtypes: bool(1), object(9)\n",
+      "memory usage: 68.4+ KB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TL       0\n",
+       "TM       0\n",
+       "TR       0\n",
+       "ML       0\n",
+       "MM       0\n",
+       "MR       0\n",
+       "BL       0\n",
+       "BM       0\n",
+       "BR       0\n",
+       "class    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tic_tac_toe.shape\n",
+    "tic_tac_toe.info()\n",
+    "tic_tac_toe.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['TL', 'TM', 'TR', 'ML', 'MM', 'MR', 'BL', 'BM', 'BR', 'class']"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['TL', 'TM', 'TR', 'ML', 'MM', 'MR', 'BL', 'BM', 'BR']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coll=list(tic_tac_toe.columns)\n",
+    "display(coll)\n",
+    "coll.remove('class')\n",
+    "coll"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import preprocessing\n",
+    "\n",
+    "le = preprocessing.LabelEncoder()\n",
+    "\n",
+    "for col in coll:\n",
+    "    le.fit(tic_tac_toe[col])\n",
+    "    tic_tac_toe[col]=le.transform(tic_tac_toe[col])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#from sklearn import preprocessing\n",
+    "## applying normalization to my dataset\n",
+    "#norm = preprocessing.MinMaxScaler()\n",
+    "#X_train = norm.fit_transform(X_train)\n",
+    "#X_test = norm.transform(X_test)\n",
+    "\n",
+    "#Normalized after splitting"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "# your code here"
+    "from sklearn import preprocessing\n",
+    "\n",
+    "\n",
+    "X = tic_tac_toe[['TL', 'TM', 'TR', 'ML','MM' , 'MR', 'BL', 'BM', 'BR']]\n",
+    "y = tic_tac_toe['class']\n",
+    "\n",
+    "\n",
+    "std = preprocessing.StandardScaler()\n",
+    "X = std.fit_transform(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.00322257,  1.08495342,  1.00322257, ...,  1.00322257,\n",
+       "        -0.16731812, -0.28682739],\n",
+       "       [ 1.00322257,  1.08495342,  1.00322257, ..., -0.28682739,\n",
+       "         1.08495342, -0.28682739],\n",
+       "       [ 1.00322257,  1.08495342,  1.00322257, ..., -0.28682739,\n",
+       "        -0.16731812,  1.00322257],\n",
+       "       ...,\n",
+       "       [-0.28682739,  1.08495342, -0.28682739, ...,  1.00322257,\n",
+       "        -0.16731812,  1.00322257],\n",
+       "       [-0.28682739,  1.08495342, -0.28682739, ...,  1.00322257,\n",
+       "        -0.16731812,  1.00322257],\n",
+       "       [-0.28682739, -0.16731812,  1.00322257, ..., -0.28682739,\n",
+       "         1.08495342,  1.00322257]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       True\n",
+       "1       True\n",
+       "2       True\n",
+       "3       True\n",
+       "4       True\n",
+       "       ...  \n",
+       "953    False\n",
+       "954    False\n",
+       "955    False\n",
+       "956    False\n",
+       "957    False\n",
+       "Name: class, Length: 958, dtype: bool"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
    ]
   },
   {
@@ -58,13 +264,348 @@
     "1. Save your model as `tic-tac-toe.model`."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.00322257, -0.16731812, -0.28682739, ...,  1.00322257,\n",
+       "        -1.41958965,  1.00322257],\n",
+       "       [-1.57687736, -1.41958965, -1.57687736, ...,  1.00322257,\n",
+       "         1.08495342,  1.00322257],\n",
+       "       [-0.28682739,  1.08495342, -1.57687736, ..., -0.28682739,\n",
+       "        -1.41958965, -0.28682739],\n",
+       "       ...,\n",
+       "       [ 1.00322257,  1.08495342, -0.28682739, ..., -0.28682739,\n",
+       "        -0.16731812, -1.57687736],\n",
+       "       [-1.57687736, -0.16731812,  1.00322257, ...,  1.00322257,\n",
+       "         1.08495342,  1.00322257],\n",
+       "       [ 1.00322257, -0.16731812,  1.00322257, ..., -0.28682739,\n",
+       "        -0.16731812, -0.28682739]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.57687736,  1.08495342,  1.00322257, ..., -0.28682739,\n",
+       "        -0.16731812, -0.28682739],\n",
+       "       [-1.57687736,  1.08495342, -0.28682739, ..., -0.28682739,\n",
+       "        -0.16731812, -1.57687736],\n",
+       "       [ 1.00322257,  1.08495342,  1.00322257, ..., -0.28682739,\n",
+       "        -0.16731812,  1.00322257],\n",
+       "       ...,\n",
+       "       [ 1.00322257, -1.41958965, -1.57687736, ...,  1.00322257,\n",
+       "        -0.16731812, -1.57687736],\n",
+       "       [ 1.00322257, -1.41958965, -1.57687736, ...,  1.00322257,\n",
+       "         1.08495342, -0.28682739],\n",
+       "       [ 1.00322257,  1.08495342,  1.00322257, ..., -0.28682739,\n",
+       "        -0.16731812, -1.57687736]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "162     True\n",
+       "624     True\n",
+       "346     True\n",
+       "571     True\n",
+       "947    False\n",
+       "       ...  \n",
+       "835    False\n",
+       "192     True\n",
+       "629    False\n",
+       "559     True\n",
+       "684    False\n",
+       "Name: class, Length: 718, dtype: bool"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense\n",
+    "from keras.utils import to_categorical"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "# your code here"
+    "model = Sequential([ # as far as we know, all networks are sequential\n",
+    "  Dense(64, activation='relu', input_shape=(9,)), # 784= 28*28 dense networks means all neurons in one layer are connected to all neuronsof the next layer\n",
+    "  Dense(64, activation='relu'),      # choosing relu instead of sigmoid, this is somewhat common\n",
+    "  Dense(10, activation='softmax'),   # the softmax actiavation is the last one to compensate for the high volume additions\n",
+    "])\n",
+    "\n",
+    "model.compile(\n",
+    "  optimizer='adam', #here we could use stochastic gradient descent, but adam is a de facto standard\n",
+    "  loss='sparse_categorical_crossentropy', #this is how we create the original blam to play the blame game\n",
+    "  metrics=['accuracy'],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/30\n",
+      "48/48 [==============================] - 0s 674us/step - loss: 1.2557 - accuracy: 0.5989\n",
+      "Epoch 2/30\n",
+      "48/48 [==============================] - 0s 537us/step - loss: 0.6503 - accuracy: 0.6838\n",
+      "Epoch 3/30\n",
+      "48/48 [==============================] - 0s 499us/step - loss: 0.5793 - accuracy: 0.7131\n",
+      "Epoch 4/30\n",
+      "48/48 [==============================] - 0s 574us/step - loss: 0.5560 - accuracy: 0.7187\n",
+      "Epoch 5/30\n",
+      "48/48 [==============================] - 0s 520us/step - loss: 0.5211 - accuracy: 0.7604\n",
+      "Epoch 6/30\n",
+      "48/48 [==============================] - 0s 499us/step - loss: 0.4908 - accuracy: 0.7925\n",
+      "Epoch 7/30\n",
+      "48/48 [==============================] - 0s 587us/step - loss: 0.4624 - accuracy: 0.7981\n",
+      "Epoch 8/30\n",
+      "48/48 [==============================] - 0s 559us/step - loss: 0.4358 - accuracy: 0.8259\n",
+      "Epoch 9/30\n",
+      "48/48 [==============================] - 0s 529us/step - loss: 0.4114 - accuracy: 0.8357\n",
+      "Epoch 10/30\n",
+      "48/48 [==============================] - 0s 571us/step - loss: 0.3881 - accuracy: 0.8440\n",
+      "Epoch 11/30\n",
+      "48/48 [==============================] - 0s 593us/step - loss: 0.3589 - accuracy: 0.8579\n",
+      "Epoch 12/30\n",
+      "48/48 [==============================] - 0s 519us/step - loss: 0.3352 - accuracy: 0.8802\n",
+      "Epoch 13/30\n",
+      "48/48 [==============================] - 0s 499us/step - loss: 0.3162 - accuracy: 0.8900\n",
+      "Epoch 14/30\n",
+      "48/48 [==============================] - 0s 585us/step - loss: 0.2945 - accuracy: 0.8914\n",
+      "Epoch 15/30\n",
+      "48/48 [==============================] - 0s 583us/step - loss: 0.2751 - accuracy: 0.9011\n",
+      "Epoch 16/30\n",
+      "48/48 [==============================] - 0s 547us/step - loss: 0.2583 - accuracy: 0.9067\n",
+      "Epoch 17/30\n",
+      "48/48 [==============================] - 0s 562us/step - loss: 0.2476 - accuracy: 0.9067\n",
+      "Epoch 18/30\n",
+      "48/48 [==============================] - 0s 597us/step - loss: 0.2244 - accuracy: 0.9262\n",
+      "Epoch 19/30\n",
+      "48/48 [==============================] - 0s 552us/step - loss: 0.2078 - accuracy: 0.9387\n",
+      "Epoch 20/30\n",
+      "48/48 [==============================] - 0s 519us/step - loss: 0.1977 - accuracy: 0.9359\n",
+      "Epoch 21/30\n",
+      "48/48 [==============================] - 0s 699us/step - loss: 0.1806 - accuracy: 0.9415\n",
+      "Epoch 22/30\n",
+      "48/48 [==============================] - 0s 506us/step - loss: 0.1681 - accuracy: 0.9526\n",
+      "Epoch 23/30\n",
+      "48/48 [==============================] - 0s 559us/step - loss: 0.1556 - accuracy: 0.9624\n",
+      "Epoch 24/30\n",
+      "48/48 [==============================] - 0s 593us/step - loss: 0.1476 - accuracy: 0.9540\n",
+      "Epoch 25/30\n",
+      "48/48 [==============================] - 0s 561us/step - loss: 0.1310 - accuracy: 0.9708\n",
+      "Epoch 26/30\n",
+      "48/48 [==============================] - 0s 603us/step - loss: 0.1219 - accuracy: 0.9777\n",
+      "Epoch 27/30\n",
+      "48/48 [==============================] - 0s 612us/step - loss: 0.1132 - accuracy: 0.9721\n",
+      "Epoch 28/30\n",
+      "48/48 [==============================] - 0s 553us/step - loss: 0.1028 - accuracy: 0.9819\n",
+      "Epoch 29/30\n",
+      "48/48 [==============================] - 0s 545us/step - loss: 0.0993 - accuracy: 0.9819\n",
+      "Epoch 30/30\n",
+      "48/48 [==============================] - 0s 572us/step - loss: 0.0883 - accuracy: 0.9861\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = model.fit(\n",
+    "  X_train,\n",
+    "  y_train, # just to make sure the outputs are not considered numeric (because, ya know, they are numbers...)\n",
+    "  epochs=30, # go 5 times through the whole dataset\n",
+    "  batch_size=15, # send 10 images at a time before you tweak the network again, to make it faster\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8/8 [==============================] - 0s 627us/step - loss: 0.2517 - accuracy: 0.8917\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[0.2517007291316986, 0.8916666507720947]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.evaluate(\n",
+    "  X_test,\n",
+    "  y_test\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From C:\\Users\\xico2\\anaconda3\\envs\\ML python 36\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
+      "WARNING:tensorflow:From C:\\Users\\xico2\\anaconda3\\envs\\ML python 36\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "This property should not be used in TensorFlow 2.0, as updates are applied automatically.\n",
+      "INFO:tensorflow:Assets written to: C:\\Users\\xico2\\IronHack\\Labs\\lab-deep-learning\\your-code\\assets\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.getcwd()\n",
+    "\n",
+    "model.save(os.getcwd())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here\n"
    ]
   },
   {
@@ -76,15 +617,79 @@
     "Now load your saved model and use it to make predictions on a few random rows in the test dataset. Check if the predictions are correct."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 1 1 1]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "879    0\n",
+       "496    1\n",
+       "14     1\n",
+       "546    1\n",
+       "55     1\n",
+       "Name: class, dtype: int32"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "import keras\n",
+    "import numpy as np\n",
+    "model = keras.models.load_model(os.getcwd())\n",
+    "\n",
+    "# Predict on the first 5 rows.\n",
+    "predictions = model.predict(X_test[:5])\n",
+    "\n",
+    "# Print our model's predictions.\n",
+    "print(np.argmax(predictions, axis=1))\n",
+    "\n",
+    "display(y_test[:5].astype(int))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -104,13 +709,206 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "import tensorflow as tf\n",
+    "model = Sequential([# as far as we know, all networks are sequential\n",
+    "  Dense(64, activation='relu', input_shape=(9,)),\n",
+    "    Dense(64, activation='relu'),# 784= 28*28 dense networks means all neurons in one layer are connected to all neuronsof the next layer\n",
+    "    Dense(64, activation='relu'),# choosing relu instead of sigmoid, this is somewhat common\n",
+    "  Dense(10, activation='softmax'),   # the softmax actiavation is the last one to compensate for the high volume additions\n",
+    "])\n",
+    "\n",
+    "opt = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
+    "\n",
+    "model.compile(\n",
+    "  optimizer=opt, #here we could use stochastic gradient descent, but adam is a de facto standard\n",
+    "  loss='sparse_categorical_crossentropy', #this is how we create the original blam to play the blame game\n",
+    "  metrics=['accuracy'],\n",
+    ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/60\n",
+      "48/48 [==============================] - 0s 768us/step - loss: 1.4611 - accuracy: 0.5251\n",
+      "Epoch 2/60\n",
+      "48/48 [==============================] - 0s 619us/step - loss: 0.6321 - accuracy: 0.6407\n",
+      "Epoch 3/60\n",
+      "48/48 [==============================] - 0s 589us/step - loss: 0.5845 - accuracy: 0.6950\n",
+      "Epoch 4/60\n",
+      "48/48 [==============================] - 0s 582us/step - loss: 0.5316 - accuracy: 0.7535\n",
+      "Epoch 5/60\n",
+      "48/48 [==============================] - 0s 723us/step - loss: 0.5083 - accuracy: 0.7604\n",
+      "Epoch 6/60\n",
+      "48/48 [==============================] - 0s 647us/step - loss: 0.4402 - accuracy: 0.8343\n",
+      "Epoch 7/60\n",
+      "48/48 [==============================] - 0s 570us/step - loss: 0.3927 - accuracy: 0.8454\n",
+      "Epoch 8/60\n",
+      "48/48 [==============================] - 0s 619us/step - loss: 0.3569 - accuracy: 0.8607\n",
+      "Epoch 9/60\n",
+      "48/48 [==============================] - 0s 593us/step - loss: 0.3001 - accuracy: 0.8928\n",
+      "Epoch 10/60\n",
+      "48/48 [==============================] - 0s 568us/step - loss: 0.2644 - accuracy: 0.9081\n",
+      "Epoch 11/60\n",
+      "48/48 [==============================] - 0s 586us/step - loss: 0.2254 - accuracy: 0.9206\n",
+      "Epoch 12/60\n",
+      "48/48 [==============================] - 0s 540us/step - loss: 0.1959 - accuracy: 0.9276\n",
+      "Epoch 13/60\n",
+      "48/48 [==============================] - 0s 594us/step - loss: 0.1658 - accuracy: 0.9485\n",
+      "Epoch 14/60\n",
+      "48/48 [==============================] - 0s 599us/step - loss: 0.1352 - accuracy: 0.9596\n",
+      "Epoch 15/60\n",
+      "48/48 [==============================] - 0s 591us/step - loss: 0.1102 - accuracy: 0.9749\n",
+      "Epoch 16/60\n",
+      "48/48 [==============================] - 0s 594us/step - loss: 0.0942 - accuracy: 0.9805\n",
+      "Epoch 17/60\n",
+      "48/48 [==============================] - 0s 548us/step - loss: 0.0692 - accuracy: 0.9875\n",
+      "Epoch 18/60\n",
+      "48/48 [==============================] - 0s 622us/step - loss: 0.0547 - accuracy: 0.9944\n",
+      "Epoch 19/60\n",
+      "48/48 [==============================] - 0s 590us/step - loss: 0.0449 - accuracy: 0.9930\n",
+      "Epoch 20/60\n",
+      "48/48 [==============================] - 0s 580us/step - loss: 0.0337 - accuracy: 0.9986\n",
+      "Epoch 21/60\n",
+      "48/48 [==============================] - 0s 569us/step - loss: 0.0276 - accuracy: 1.0000\n",
+      "Epoch 22/60\n",
+      "48/48 [==============================] - 0s 583us/step - loss: 0.0213 - accuracy: 1.0000\n",
+      "Epoch 23/60\n",
+      "48/48 [==============================] - 0s 734us/step - loss: 0.0192 - accuracy: 1.0000\n",
+      "Epoch 24/60\n",
+      "48/48 [==============================] - 0s 556us/step - loss: 0.0150 - accuracy: 1.0000\n",
+      "Epoch 25/60\n",
+      "48/48 [==============================] - 0s 676us/step - loss: 0.0119 - accuracy: 1.0000\n",
+      "Epoch 26/60\n",
+      "48/48 [==============================] - 0s 674us/step - loss: 0.0096 - accuracy: 1.0000\n",
+      "Epoch 27/60\n",
+      "48/48 [==============================] - 0s 591us/step - loss: 0.0081 - accuracy: 1.0000\n",
+      "Epoch 28/60\n",
+      "48/48 [==============================] - 0s 663us/step - loss: 0.0071 - accuracy: 1.0000\n",
+      "Epoch 29/60\n",
+      "48/48 [==============================] - 0s 596us/step - loss: 0.0062 - accuracy: 1.0000\n",
+      "Epoch 30/60\n",
+      "48/48 [==============================] - 0s 586us/step - loss: 0.0054 - accuracy: 1.0000\n",
+      "Epoch 31/60\n",
+      "48/48 [==============================] - 0s 588us/step - loss: 0.0047 - accuracy: 1.0000\n",
+      "Epoch 32/60\n",
+      "48/48 [==============================] - 0s 631us/step - loss: 0.0042 - accuracy: 1.0000\n",
+      "Epoch 33/60\n",
+      "48/48 [==============================] - 0s 650us/step - loss: 0.0037 - accuracy: 1.0000\n",
+      "Epoch 34/60\n",
+      "48/48 [==============================] - 0s 664us/step - loss: 0.0033 - accuracy: 1.0000\n",
+      "Epoch 35/60\n",
+      "48/48 [==============================] - 0s 592us/step - loss: 0.0030 - accuracy: 1.0000\n",
+      "Epoch 36/60\n",
+      "48/48 [==============================] - 0s 568us/step - loss: 0.0026 - accuracy: 1.0000\n",
+      "Epoch 37/60\n",
+      "48/48 [==============================] - 0s 760us/step - loss: 0.0024 - accuracy: 1.0000\n",
+      "Epoch 38/60\n",
+      "48/48 [==============================] - 0s 605us/step - loss: 0.0021 - accuracy: 1.0000\n",
+      "Epoch 39/60\n",
+      "48/48 [==============================] - 0s 594us/step - loss: 0.0020 - accuracy: 1.0000\n",
+      "Epoch 40/60\n",
+      "48/48 [==============================] - 0s 707us/step - loss: 0.0018 - accuracy: 1.0000\n",
+      "Epoch 41/60\n",
+      "48/48 [==============================] - 0s 576us/step - loss: 0.0016 - accuracy: 1.0000\n",
+      "Epoch 42/60\n",
+      "48/48 [==============================] - 0s 627us/step - loss: 0.0015 - accuracy: 1.0000\n",
+      "Epoch 43/60\n",
+      "48/48 [==============================] - 0s 560us/step - loss: 0.0014 - accuracy: 1.0000\n",
+      "Epoch 44/60\n",
+      "48/48 [==============================] - 0s 582us/step - loss: 0.0013 - accuracy: 1.0000\n",
+      "Epoch 45/60\n",
+      "48/48 [==============================] - 0s 596us/step - loss: 0.0012 - accuracy: 1.0000\n",
+      "Epoch 46/60\n",
+      "48/48 [==============================] - 0s 565us/step - loss: 0.0011 - accuracy: 1.0000\n",
+      "Epoch 47/60\n",
+      "48/48 [==============================] - 0s 685us/step - loss: 0.0010 - accuracy: 1.0000\n",
+      "Epoch 48/60\n",
+      "48/48 [==============================] - 0s 584us/step - loss: 9.6424e-04 - accuracy: 1.0000\n",
+      "Epoch 49/60\n",
+      "48/48 [==============================] - 0s 740us/step - loss: 8.9443e-04 - accuracy: 1.0000\n",
+      "Epoch 50/60\n",
+      "48/48 [==============================] - 0s 598us/step - loss: 8.3055e-04 - accuracy: 1.0000\n",
+      "Epoch 51/60\n",
+      "48/48 [==============================] - 0s 647us/step - loss: 7.9388e-04 - accuracy: 1.0000\n",
+      "Epoch 52/60\n",
+      "48/48 [==============================] - 0s 603us/step - loss: 7.3674e-04 - accuracy: 1.0000\n",
+      "Epoch 53/60\n",
+      "48/48 [==============================] - 0s 644us/step - loss: 6.8690e-04 - accuracy: 1.0000\n",
+      "Epoch 54/60\n",
+      "48/48 [==============================] - 0s 727us/step - loss: 6.4070e-04 - accuracy: 1.0000\n",
+      "Epoch 55/60\n",
+      "48/48 [==============================] - 0s 617us/step - loss: 6.1734e-04 - accuracy: 1.0000\n",
+      "Epoch 56/60\n",
+      "48/48 [==============================] - 0s 621us/step - loss: 5.8071e-04 - accuracy: 1.0000\n",
+      "Epoch 57/60\n",
+      "48/48 [==============================] - 0s 626us/step - loss: 5.4303e-04 - accuracy: 1.0000\n",
+      "Epoch 58/60\n",
+      "48/48 [==============================] - 0s 645us/step - loss: 5.1229e-04 - accuracy: 1.0000\n",
+      "Epoch 59/60\n",
+      "48/48 [==============================] - 0s 584us/step - loss: 4.8246e-04 - accuracy: 1.0000\n",
+      "Epoch 60/60\n",
+      "48/48 [==============================] - 0s 617us/step - loss: 4.5750e-04 - accuracy: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "history = model.fit(\n",
+    "  X_train,\n",
+    "  y_train, # just to make sure the outputs are not considered numeric (because, ya know, they are numbers...)\n",
+    "  epochs=60, # go 5 times through the whole dataset\n",
+    "  batch_size=15, # send 10 images at a time before you tweak the network again, to make it faster\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8/8 [==============================] - 0s 759us/step - loss: 0.1368 - accuracy: 0.9625\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[0.13681451976299286, 0.9624999761581421]"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.evaluate(\n",
+    "  X_test,\n",
+    "  y_test\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -120,12 +918,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your answer here"
+    "# your answer here\n",
+    "#I could see that by adding more hidden layers it increases that accuracy very quickly right in the first epochs\n",
+    "#As we increase the learning rate from 0.01 to 0.10 the model get's stuck on a nrealy 0.65 and do not evolves along the epochs\n",
+    "#I also tweaked the number of epochs to a higer value\n",
+    "\n",
+    "#Still I didn't figured out what would be the most appropriate neural network features/schema to this problem! A mini class would be awsome!"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -144,7 +961,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.6.12"
   }
  },
  "nbformat": 4,
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