diff --git a/lab/2001_Python.ipynb b/lab/01_Python.ipynb
similarity index 63%
rename from lab/2001_Python.ipynb
rename to lab/01_Python.ipynb
index 5aebe15..60bb260 100644
--- a/lab/2001_Python.ipynb
+++ b/lab/01_Python.ipynb
@@ -8,8 +8,8 @@
     }
    },
    "source": [
-    "## Uczenie maszynowe 2019/2020 – laboratoria\n",
-    "### 2/3 marca 2020\n",
+    "## Uczenie maszynowe 2020/2021 – laboratoria\n",
+    "### 3 marca 2021\n",
     "# 1. Python – listy składane, indeksowanie, biblioteka _NumPy_"
    ]
   },
@@ -20,6 +20,45 @@
     "## Listy składane (*List comprehension*)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]\n"
+     ]
+    }
+   ],
+   "source": [
+    "lista = []\n",
+    "for x in range(1, 11):\n",
+    "    lista.append(x ** 2)\n",
+    "    \n",
+    "print(lista)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]\n"
+     ]
+    }
+   ],
+   "source": [
+    "lista = [x ** 2 for x in range(1, 11)]\n",
+    "print(lista)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -27,6 +66,143 @@
     "Przypuśćmy, że mamy dane zdanie i chcemy utworzyć listę, która będzie zawierać długości kolejnych wyrazów tego zdania. Możemy to zrobić w następujący sposób:"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[4, 16, 36, 64, 100]\n"
+     ]
+    }
+   ],
+   "source": [
+    "lista = []\n",
+    "for i in range(1, 11):\n",
+    "    if i % 2 == 0:\n",
+    "        lista.append(i ** 2)\n",
+    "    \n",
+    "print(lista)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[4, 16, 36, 64, 100]\n"
+     ]
+    }
+   ],
+   "source": [
+    "lista = [i ** 2 for i in range(1, 11) if i % 2 == 0]\n",
+    "print(lista)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(range(1, 11))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tracz tarł tarcicę tak takt w takt jak takt w takt tarcicę tartak tarł\n"
+     ]
+    }
+   ],
+   "source": [
+    "zdanie = 'tracz tarł tarcicę tak takt w takt jak takt w takt tarcicę tartak tarł'\n",
+    "print(zdanie)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['tracz', 'tarł', 'tarcicę', 'tak', 'takt', 'w', 'takt', 'jak', 'takt', 'w', 'takt', 'tarcicę', 'tartak', 'tarł']\n"
+     ]
+    }
+   ],
+   "source": [
+    "wyrazy = zdanie.split()\n",
+    "print(wyrazy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5, 4, 7, 3, 4, 1, 4, 3, 4, 1, 4, 7, 6, 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "dlugosci_wyrazow = []\n",
+    "for wyraz in wyrazy:\n",
+    "    dlugosci_wyrazow.append(len(wyraz))\n",
+    "    \n",
+    "print(dlugosci_wyrazow)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5, 4, 7, 3, 4, 1, 4, 3, 4, 1, 4, 7, 6, 4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "dlugosci_wyrazow = [len(wyraz) for wyraz in wyrazy]\n",
+    "print(dlugosci_wyrazow)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -59,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -72,8 +248,7 @@
    ],
    "source": [
     "zdanie = 'tracz tarł tarcicę tak takt w takt jak takt w takt tarcicę tartak tarł'\n",
-    "wyrazy = zdanie.split()\n",
-    "dlugosci_wyrazow = [len(wyraz) for wyraz in wyrazy]\n",
+    "dlugosci_wyrazow = [len(wyraz) for wyraz in zdanie.split()]\n",
     "\n",
     "print(dlugosci_wyrazow)"
    ]
@@ -121,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -148,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -177,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -205,6 +380,23 @@
     "print(napis[:])  # 'abcde' (kopia całego napisu)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "abcde\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(napis[:])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -236,7 +428,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3]\n",
+      " [4 5 6]\n",
+      " [7 8 9]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "A = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
+    "\n",
+    "print(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
@@ -256,6 +471,119 @@
     "print(x)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[  1.     2.     3.    -4.5    5.  ]\n",
+      " [ 10.     9.    -8.   -13.     0.39]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "y = np.array([[1, 2, 3, -4.5, 5], [10, 9, -8, -13, 0.39]])\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[  1.     2.  ]\n",
+      " [  3.    -4.5 ]\n",
+      " [  5.    10.  ]\n",
+      " [  9.    -8.  ]\n",
+      " [-13.     0.39]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "z = y.reshape(5, 2)\n",
+    "print(z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(z.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[  1.     2.     3.    -4.5    5.  ]\n",
+      " [ 10.     9.    -8.   -13.     0.39]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "w = z.reshape(2, 5)\n",
+    "print(w)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -265,22 +593,146 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(3, 3)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 5)\n",
+      "[[  1.     2.  ]\n",
+      " [  3.     4.  ]\n",
+      " [  5.    10.  ]\n",
+      " [  9.     8.  ]\n",
+      " [-13.     0.39]]\n",
+      "(5, 2)\n"
+     ]
     }
    ],
    "source": [
-    "x.shape"
+    "print(y.shape)\n",
+    "\n",
+    "z = y.reshape((5, 2))\n",
+    "print(z)\n",
+    "print(z.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[  1.     2.  ]\n",
+      " [  3.    -4.5 ]\n",
+      " [  5.    10.  ]\n",
+      " [  9.    -8.  ]\n",
+      " [-13.     0.39]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  3.    -1.5   15.     1.   -12.61]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(z.sum(axis=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3]\n",
+      " [4 5 6]\n",
+      " [7 8 9]]\n",
+      "[2. 5. 8.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x)\n",
+    "print(x.mean(axis=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1  2  3  4  5  6  7  8  9 10]\n"
+     ]
+    }
+   ],
+   "source": [
+    "lista = list(range(1, 11))\n",
+    "A = np.array(lista)\n",
+    "print(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
+      "[ 1  2  3  4  5  6  7  8  9 10]\n"
+     ]
+    }
+   ],
+   "source": [
+    "lista = list(range(1, 11))\n",
+    "print(lista)\n",
+    "A = np.array(lista)\n",
+    "print(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1  2  3  4  5  6  7  8  9 10]\n"
+     ]
+    }
+   ],
+   "source": [
+    "A = np.arange(1, 11)\n",
+    "print(A)"
    ]
   },
   {
@@ -352,42 +804,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([5, 6, 7, 8, 9])"
+       "array([ 5,  6,  7,  8,  9, 10, 11, 12, 13, 14])"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "np.arange(5, 10)"
+    "np.arange(5, 15)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([5. , 5.5, 6. , 6.5, 7. , 7.5, 8. , 8.5, 9. , 9.5])"
+       "array([[ 5. ,  5.1,  5.2,  5.3,  5.4,  5.5,  5.6,  5.7,  5.8,  5.9],\n",
+       "       [ 6. ,  6.1,  6.2,  6.3,  6.4,  6.5,  6.6,  6.7,  6.8,  6.9],\n",
+       "       [ 7. ,  7.1,  7.2,  7.3,  7.4,  7.5,  7.6,  7.7,  7.8,  7.9],\n",
+       "       [ 8. ,  8.1,  8.2,  8.3,  8.4,  8.5,  8.6,  8.7,  8.8,  8.9],\n",
+       "       [ 9. ,  9.1,  9.2,  9.3,  9.4,  9.5,  9.6,  9.7,  9.8,  9.9],\n",
+       "       [10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8, 10.9],\n",
+       "       [11. , 11.1, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7, 11.8, 11.9],\n",
+       "       [12. , 12.1, 12.2, 12.3, 12.4, 12.5, 12.6, 12.7, 12.8, 12.9],\n",
+       "       [13. , 13.1, 13.2, 13.3, 13.4, 13.5, 13.6, 13.7, 13.8, 13.9],\n",
+       "       [14. , 14.1, 14.2, 14.3, 14.4, 14.5, 14.6, 14.7, 14.8, 14.9]])"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "np.arange(5, 10, 0.5)"
+    "np.arange(5, 15, 0.1).reshape(10, 10)"
    ]
   },
   {
@@ -431,19 +892,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.   1.25 2.5  3.75 5.  ]\n"
+      "[0.         0.22222222 0.44444444 0.66666667 0.88888889 1.11111111\n",
+      " 1.33333333 1.55555556 1.77777778 2.        ]\n"
      ]
     }
    ],
    "source": [
-    "x = np.linspace(0, 5, 5)\n",
+    "x = np.linspace(0, 2, 10)\n",
     "print(x)"
    ]
   },
@@ -517,14 +979,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "int32\n",
+      "float64\n",
       "[0.1 0.2 0.3]\n",
       "float64\n",
       "float64\n"
@@ -532,7 +994,7 @@
     }
    ],
    "source": [
-    "x = np.array([1, 2, 3])\n",
+    "x = np.array([1, 2, 3, 0.5])\n",
     "print(x.dtype)\n",
     "x = np.array([0.1, 0.2, 0.3])\n",
     "print(x)\n",
@@ -550,7 +1012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
@@ -570,7 +1032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
@@ -584,10 +1046,213 @@
     }
    ],
    "source": [
-    "x = np.ones([3,4])\n",
+    "y = np.ones([3,4])\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1.]\n",
+      " [1. 1. 1. 1.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x + y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0  1  2]\n",
+      " [ 3  4  5]\n",
+      " [ 6  7  8]\n",
+      " [ 9 10 11]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = np.arange(12).reshape(4, 3)\n",
     "print(x)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 5  6  7]\n",
+      " [ 8  9 10]\n",
+      " [11 12 13]\n",
+      " [14 15 16]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "y = np.arange(5, 17).reshape(4, 3)\n",
+    "print(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[  0   6  14]\n",
+      " [ 24  36  50]\n",
+      " [ 66  84 104]\n",
+      " [126 150 176]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x * y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0 1 2 3]\n",
+      " [4 5 6 7]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "A = np.arange(8).reshape(2, 4)\n",
+    "print(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1  2  3]\n",
+      " [ 4  5  6]\n",
+      " [ 7  8  9]\n",
+      " [10 11 12]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "B = np.arange(1, 13).reshape(4, 3)\n",
+    "print(B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 4) (4, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(A.shape, B.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 48  54  60]\n",
+      " [136 158 180]]\n",
+      "(2, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "C = np.matmul(A, B)\n",
+    "print(C)\n",
+    "print(C.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "operands could not be broadcast together with shapes (2,4) (4,3) ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-150-a4cedde81ed0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mA\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mB\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,4) (4,3) "
+     ]
+    }
+   ],
+   "source": [
+    "A * B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 2 is different from 4)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-151-89ae99960b6e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mA\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 2 is different from 4)"
+     ]
+    }
+   ],
+   "source": [
+    "np.matmul(A, C)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -602,6 +1267,186 @@
     "Operatory arytmetyczne na tablicach w NumPy działają **element po elemencie**."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3]\n",
+      " [1 2 3]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "A = np.array([[1, 2, 3], [1, 2, 3]])\n",
+    "B = np.array([[4, 5, 6], [7, 8, 9]])\n",
+    "              \n",
+    "print(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[4 5 6]\n",
+      " [7 8 9]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-3 -3 -3]\n",
+      " [-6 -6 -6]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(A - B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[3 3 3]\n",
+      " [6 6 6]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(B - A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 4 10 18]\n",
+      " [ 7 16 27]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(A * B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 2 is different from 3)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-65-04c68bb92949>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mA\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mB\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 2 is different from 3)"
+     ]
+    }
+   ],
+   "source": [
+    "np.matmul(A, B)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 3)\n",
+      "(3, 4)\n",
+      "\n",
+      "[[1 2 3]\n",
+      " [1 2 3]]\n",
+      "[[1 2 3 4]\n",
+      " [9 8 7 6]\n",
+      " [2 4 6 7]]\n",
+      "[[25 30 35 37]\n",
+      " [25 30 35 37]]\n",
+      "\n",
+      "(2, 3) (3, 4)\n",
+      "(2, 4)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(A.shape)\n",
+    "B = np.array([[1, 2, 3, 4], [9, 8, 7, 6], [2, 4, 6, 7]])\n",
+    "print(B.shape)\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "print(A)\n",
+    "print(B)\n",
+    "C = np.matmul(A, B)\n",
+    "print(C)\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "print(A.shape, B.shape)\n",
+    "print(C.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[25, 30, 35, 37],\n",
+       "       [25, 30, 35, 37]])"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.dot(A, B)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 20,
@@ -1637,7 +2482,7 @@
     "        6 \\\\\n",
     "      \\end{array}\n",
     "    \\right]$ oblicz wynikowy wektor: \n",
-    "$$ \\theta = (X^\\top \\, X)^{-1} \\, X^\\top \\, y^\\top \\, . $$\n",
+    "$$ \\theta = (X^\\top \\, X)^{-1} \\, X^\\top \\, y \\, . $$\n",
     "Wykonaj te same obliczenia raz na obiektach typu `array`, a raz na obiektach typu `matrix`.\n",
     "W przypadku obiektów typu `matrix` zastosuj możliwie krótki zapis. "
    ]
diff --git a/lab/2002_Wczytywanie_i_prezentowanie_danych.ipynb b/lab/02_Wczytywanie_i_prezentowanie_danych.ipynb
similarity index 100%
rename from lab/2002_Wczytywanie_i_prezentowanie_danych.ipynb
rename to lab/02_Wczytywanie_i_prezentowanie_danych.ipynb
diff --git a/lab/2003_Regresja_liniowa.ipynb b/lab/03_Regresja_liniowa.ipynb
similarity index 100%
rename from lab/2003_Regresja_liniowa.ipynb
rename to lab/03_Regresja_liniowa.ipynb
diff --git a/lab/2007_scikit-learn.ipynb b/lab/07_scikit-learn.ipynb
similarity index 100%
rename from lab/2007_scikit-learn.ipynb
rename to lab/07_scikit-learn.ipynb
diff --git a/lab/2010_Sieci_neuronowe.ipynb b/lab/10_Sieci_neuronowe.ipynb
similarity index 100%
rename from lab/2010_Sieci_neuronowe.ipynb
rename to lab/10_Sieci_neuronowe.ipynb
diff --git a/lab/2011_Wielowarstwowe_sieci_neuronowe.ipynb b/lab/11_Wielowarstwowe_sieci_neuronowe.ipynb
similarity index 100%
rename from lab/2011_Wielowarstwowe_sieci_neuronowe.ipynb
rename to lab/11_Wielowarstwowe_sieci_neuronowe.ipynb
diff --git a/wyk/01_Wrpowadzenie.ipynb b/wyk/01_Wprowadzenie.ipynb
similarity index 99%
rename from wyk/01_Wrpowadzenie.ipynb
rename to wyk/01_Wprowadzenie.ipynb
index bb621f1..23a001a 100644
--- a/wyk/01_Wrpowadzenie.ipynb
+++ b/wyk/01_Wprowadzenie.ipynb
@@ -13,17 +13,6 @@
     "# 1. Wprowadzenie"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "https://eduwiki.wmi.amu.edu.pl"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {