diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 46f5aa14..52ff36ab 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -16,11 +16,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "### [your code here]\n"
+    "### [your code here]\n",
+    "import numpy as np\n"
    ]
   },
   {
@@ -34,11 +35,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NumPy version:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'2.3.3'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "### [your code here]\n"
+    "### [your code here]\n",
+    "print(\"NumPy version:\")\n",
+    "np.__version__"
    ]
   },
   {
@@ -51,11 +72,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "a:\n"
+     ]
+    }
+   ],
    "source": [
-    "### [your code here]\n"
+    "### [your code here]\n",
+    "a = np.random.random((2, 3, 5)) # (rows, columns)\n",
+    "\n",
+    "# Print the generated vector\n",
+    "print(\"\\na:\")\n"
    ]
   },
   {
@@ -68,11 +102,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[0.94891185 0.69934772 0.95392324 0.06408272 0.66659779]\n",
+      "  [0.68748058 0.43671501 0.80566322 0.04406075 0.33400431]\n",
+      "  [0.74132223 0.15778053 0.27293981 0.52482532 0.77690994]]\n",
+      "\n",
+      " [[0.50470868 0.91071293 0.19173581 0.09140001 0.26888879]\n",
+      "  [0.92337024 0.1057429  0.3201242  0.63731464 0.01616369]\n",
+      "  [0.60349597 0.33638534 0.16213903 0.31423468 0.53367144]]]\n"
+     ]
+    }
+   ],
    "source": [
-    "### [your code here]\n"
+    "### [your code here]\n",
+    "print(a)"
    ]
   },
   {
@@ -85,11 +134,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[1., 1., 1.],\n",
+       "        [1., 1., 1.]],\n",
+       "\n",
+       "       [[1., 1., 1.],\n",
+       "        [1., 1., 1.]],\n",
+       "\n",
+       "       [[1., 1., 1.],\n",
+       "        [1., 1., 1.]],\n",
+       "\n",
+       "       [[1., 1., 1.],\n",
+       "        [1., 1., 1.]],\n",
+       "\n",
+       "       [[1., 1., 1.],\n",
+       "        [1., 1., 1.]]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "### [your code here]\n"
+    "### [your code here]\n",
+    "b = np.ones((5, 2, 3)) # (rows, columns)\n",
+    "np.ones((5, 2, 3))"
    ]
   },
   {
@@ -102,11 +177,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "b:\n",
+      "[[[1. 1. 1.]\n",
+      "  [1. 1. 1.]]\n",
+      "\n",
+      " [[1. 1. 1.]\n",
+      "  [1. 1. 1.]]\n",
+      "\n",
+      " [[1. 1. 1.]\n",
+      "  [1. 1. 1.]]\n",
+      "\n",
+      " [[1. 1. 1.]\n",
+      "  [1. 1. 1.]]\n",
+      "\n",
+      " [[1. 1. 1.]\n",
+      "  [1. 1. 1.]]]\n"
+     ]
+    }
+   ],
    "source": [
-    "### [your code here]\n"
+    "### [your code here]\n",
+    "b = np.ones((5, 2, 3)) # (rows, columns)\n",
+    "\n",
+    "# Print the generated vector\n",
+    "print(\"\\nb:\")\n",
+    "print(b)"
    ]
   },
   {
@@ -119,11 +222,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30\n",
+      "30\n"
+     ]
+    }
+   ],
    "source": [
-    "### [your code here]\n"
+    "### [your code here]\n",
+    "print(a.size)\n",
+    "print(b.size)\n"
    ]
   },
   {
@@ -136,11 +250,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 3, 5)\n",
+      "(5, 2, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "### [your code here]\n",
+    "print(a.shape)\n",
+    "print(b.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "### [your code here]\n"
+    "#The 2 Tensor are not compatible because shape is different"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 2, 3)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.shape"
    ]
   },
   {
@@ -154,11 +308,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[1. 1. 1. 1. 1.]\n",
+      "  [1. 1. 1. 1. 1.]\n",
+      "  [1. 1. 1. 1. 1.]]\n",
+      "\n",
+      " [[1. 1. 1. 1. 1.]\n",
+      "  [1. 1. 1. 1. 1.]\n",
+      "  [1. 1. 1. 1. 1.]]]\n"
+     ]
+    }
+   ],
    "source": [
-    "### [your code here]\n"
+    "### [your code here]\n",
+    "c = np.transpose(b, (1, 2, 0))\n",
+    "print(c)"
    ]
   },
   {
@@ -171,11 +341,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[1.94891185 1.69934772 1.95392324 1.06408272 1.66659779]\n",
+      "  [1.68748058 1.43671501 1.80566322 1.04406075 1.33400431]\n",
+      "  [1.74132223 1.15778053 1.27293981 1.52482532 1.77690994]]\n",
+      "\n",
+      " [[1.50470868 1.91071293 1.19173581 1.09140001 1.26888879]\n",
+      "  [1.92337024 1.1057429  1.3201242  1.63731464 1.01616369]\n",
+      "  [1.60349597 1.33638534 1.16213903 1.31423468 1.53367144]]]\n"
+     ]
+    }
+   ],
    "source": [
-    "### [your code here]\n"
+    "### [your code here]\n",
+    "d = a+c\n",
+    "print(d)"
    ]
   },
   {
@@ -188,12 +374,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a:\n",
+      "[[[0.94891185 0.69934772 0.95392324 0.06408272 0.66659779]\n",
+      "  [0.68748058 0.43671501 0.80566322 0.04406075 0.33400431]\n",
+      "  [0.74132223 0.15778053 0.27293981 0.52482532 0.77690994]]\n",
+      "\n",
+      " [[0.50470868 0.91071293 0.19173581 0.09140001 0.26888879]\n",
+      "  [0.92337024 0.1057429  0.3201242  0.63731464 0.01616369]\n",
+      "  [0.60349597 0.33638534 0.16213903 0.31423468 0.53367144]]]\n",
+      "d:\n",
+      "[[[1.94891185 1.69934772 1.95392324 1.06408272 1.66659779]\n",
+      "  [1.68748058 1.43671501 1.80566322 1.04406075 1.33400431]\n",
+      "  [1.74132223 1.15778053 1.27293981 1.52482532 1.77690994]]\n",
+      "\n",
+      " [[1.50470868 1.91071293 1.19173581 1.09140001 1.26888879]\n",
+      "  [1.92337024 1.1057429  1.3201242  1.63731464 1.01616369]\n",
+      "  [1.60349597 1.33638534 1.16213903 1.31423468 1.53367144]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "### [your code here]\n",
+    "print(\"a:\")\n",
+    "print(a)\n",
+    "print(\"d:\")\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "### [your code here]\n",
-    "\n"
+    "The values of tensor d are the result of the sum a+b"
    ]
   },
   {
@@ -206,11 +427,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[0.94891185, 0.69934772, 0.95392324, 0.06408272, 0.66659779],\n",
+       "        [0.68748058, 0.43671501, 0.80566322, 0.04406075, 0.33400431],\n",
+       "        [0.74132223, 0.15778053, 0.27293981, 0.52482532, 0.77690994]],\n",
+       "\n",
+       "       [[0.50470868, 0.91071293, 0.19173581, 0.09140001, 0.26888879],\n",
+       "        [0.92337024, 0.1057429 , 0.3201242 , 0.63731464, 0.01616369],\n",
+       "        [0.60349597, 0.33638534, 0.16213903, 0.31423468, 0.53367144]]])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "### [your code here]\n"
+    "### [your code here]\n",
+    "e = a * c"
    ]
   },
   {
@@ -224,12 +463,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "### [your code here]\n",
-    "\n"
+    "\n",
+    "e is equal to a, because the Tensor b has only 1 numbers"
    ]
   },
   {
@@ -243,12 +483,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[1.94891185 1.69934772 1.95392324 1.06408272 1.66659779]\n",
+      "  [1.68748058 1.43671501 1.80566322 1.04406075 1.33400431]\n",
+      "  [1.74132223 1.15778053 1.27293981 1.52482532 1.77690994]]\n",
+      "\n",
+      " [[1.50470868 1.91071293 1.19173581 1.09140001 1.26888879]\n",
+      "  [1.92337024 1.1057429  1.3201242  1.63731464 1.01616369]\n",
+      "  [1.60349597 1.33638534 1.16213903 1.31423468 1.53367144]]]\n"
+     ]
+    }
+   ],
    "source": [
     "### [your code here]\n",
-    "\n"
+    "\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.953923235618411\n"
+     ]
+    }
+   ],
+   "source": [
+    "d_max = d.max()\n",
+    "print(d_max)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.016163692153468\n"
+     ]
+    }
+   ],
+   "source": [
+    "d_min = d.min()\n",
+    "print(d_min)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.4678217792962374\n"
+     ]
+    }
+   ],
+   "source": [
+    "d_mean = d.mean()\n",
+    "print(d_mean)"
    ]
   },
   {
@@ -261,19 +570,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[1.94891185 1.69934772 1.95392324 1.06408272 1.66659779]\n",
+      "  [1.68748058 1.43671501 1.80566322 1.04406075 1.33400431]\n",
+      "  [1.74132223 1.15778053 1.27293981 1.52482532 1.77690994]]\n",
+      "\n",
+      " [[1.50470868 1.91071293 1.19173581 1.09140001 1.26888879]\n",
+      "  [1.92337024 1.1057429  1.3201242  1.63731464 1.01616369]\n",
+      "  [1.60349597 1.33638534 1.16213903 1.31423468 1.53367144]]]\n"
+     ]
+    }
+   ],
    "source": [
-    "### [your code here]\n"
+    "### [your code here]\n",
+    "f = np.empty(d.shape)\n",
+    "print(f)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
-    "\n",
     "\n",
     "### 16. Populate the values in f. For each value in d, if it's larger than d_min but smaller than d_mean, assign 25 to the corresponding value in f.\n",
     "If a value in d is larger than d_mean but smaller than d_max, assign 75 to the corresponding value in f.\n",
@@ -287,20 +610,121 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[ 75  75 100  25  75]\n",
+      "  [ 75  25  75  25  25]\n",
+      "  [ 75  25  25  75  75]]\n",
+      "\n",
+      " [[ 75  75  25  25  25]\n",
+      "  [ 75  25  25  75   0]\n",
+      "  [ 75  25  25  25  75]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def populate_f(x):\n",
+    "    if x == d_min:\n",
+    "        return 0\n",
+    "    elif x == d_max:\n",
+    "        return 100\n",
+    "    elif x == d_mean:\n",
+    "        return 50\n",
+    "    elif d_min < x < d_mean:\n",
+    "        return 25\n",
+    "    else:\n",
+    "        return 75\n",
+    "f = np.vectorize(populate_f)(d)\n",
+    "print(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[False, False, False, False, False],\n",
+       "        [False, False, False, False, False],\n",
+       "        [False, False, False, False, False]],\n",
+       "\n",
+       "       [[False, False, False, False, False],\n",
+       "        [False, False, False, False, False],\n",
+       "        [False, False, False, False, False]]])"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d == d_mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[False, False, False, False, False],\n",
+       "        [False, False, False, False, False],\n",
+       "        [False, False, False, False, False]],\n",
+       "\n",
+       "       [[False, False, False, False, False],\n",
+       "        [False, False, False, False,  True],\n",
+       "        [False, False, False, False, False]]])"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d == d_min "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[False, False,  True, False, False],\n",
+       "        [False, False, False, False, False],\n",
+       "        [False, False, False, False, False]],\n",
+       "\n",
+       "       [[False, False, False, False, False],\n",
+       "        [False, False, False, False, False],\n",
+       "        [False, False, False, False, False]]])"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d == d_max"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
-    "\n",
-    "\n",
     "\n",
     "### 17. Print d and f. Do you have your expected f?\n",
     "For instance, if your d is:\n",
@@ -325,11 +749,129 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "d:\n",
+      "[[[1.85836099 1.67064465 1.62576044 1.40243961 1.88454931]\n",
+      "  [1.75354326 1.69403643 1.36729252 1.61415071 1.12104981]\n",
+      "  [1.72201435 1.1862918  1.87078449 1.7726778  1.88180042]]\n",
+      "\n",
+      " [[1.44747908 1.31673383 1.02000951 1.52218947 1.97066381]\n",
+      "  [1.79129243 1.74983003 1.96028037 1.85166831 1.65450881]\n",
+      "  [1.18068344 1.9587381  1.00656599 1.93402165 1.73514584]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "### [your code here]\n",
+    "\n",
+    "e = np.array([[[1.85836099, 1.67064465, 1.62576044, 1.40243961, 1.88454931],\n",
+    "                [1.75354326, 1.69403643, 1.36729252, 1.61415071, 1.12104981],\n",
+    "                [1.72201435, 1.1862918 , 1.87078449, 1.7726778 , 1.88180042]],\n",
+    "\n",
+    "                [[1.44747908, 1.31673383, 1.02000951, 1.52218947, 1.97066381],\n",
+    "                [1.79129243, 1.74983003, 1.96028037, 1.85166831, 1.65450881],\n",
+    "                [1.18068344, 1.9587381 , 1.00656599, 1.93402165, 1.73514584]]])\n",
+    "d = e\n",
+    "print(\"d:\")\n",
+    "print(d)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.6175069086666667\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "d_mean = d.mean()\n",
+    "print(d_mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.00656599\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "d_min = d.min()\n",
+    "print(d_min)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.97066381\n"
+     ]
+    }
+   ],
    "source": [
-    "### [your code here]\n"
+    "d_max = d.max()\n",
+    "print(d_max)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[ 75  75  75  25  75]\n",
+      "  [ 75  75  25  25  25]\n",
+      "  [ 75  25  75  75  75]]\n",
+      "\n",
+      " [[ 25  25  25  25 100]\n",
+      "  [ 75  75  75  75  75]\n",
+      "  [ 25  75   0  75  75]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def populate_f(x):\n",
+    "    if x == d_min:\n",
+    "        return 0\n",
+    "    elif x == d_max:\n",
+    "        return 100\n",
+    "    elif x == d_mean:\n",
+    "        return 50\n",
+    "    elif d_min < x < d_mean:\n",
+    "        return 25\n",
+    "    else:\n",
+    "        return 75\n",
+    "f = np.vectorize(populate_f)(d)\n",
+    "print(f)"
    ]
   },
   {
@@ -374,7 +916,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.0"
+   "version": "3.13.8"
   }
  },
  "nbformat": 4,