diff --git a/wyk/10_Propagacja_wsteczna.ipynb b/wyk/10_Propagacja_wsteczna.ipynb
index 33f96fe..879468c 100644
--- a/wyk/10_Propagacja_wsteczna.ipynb
+++ b/wyk/10_Propagacja_wsteczna.ipynb
@@ -858,7 +858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 1,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -878,7 +878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "notes"
@@ -901,7 +901,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -934,7 +934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -969,7 +969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 9,
    "metadata": {
     "scrolled": true,
     "slideshow": {
@@ -981,19 +981,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"sequential_21\"\n",
+      "Model: \"sequential_1\"\n",
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
-      "dense_59 (Dense)             (None, 512)               401920    \n",
+      "dense_3 (Dense)              (None, 512)               401920    \n",
       "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 512)               0         \n",
+      "dropout (Dropout)            (None, 512)               0         \n",
       "_________________________________________________________________\n",
-      "dense_60 (Dense)             (None, 512)               262656    \n",
+      "dense_4 (Dense)              (None, 512)               262656    \n",
       "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 512)               0         \n",
+      "dropout_1 (Dropout)          (None, 512)               0         \n",
       "_________________________________________________________________\n",
-      "dense_61 (Dense)             (None, 10)                5130      \n",
+      "dense_5 (Dense)              (None, 10)                5130      \n",
       "=================================================================\n",
       "Total params: 669,706\n",
       "Trainable params: 669,706\n",
@@ -1009,12 +1009,13 @@
     "model.add(Dense(512, activation='relu'))\n",
     "model.add(Dropout(0.2))\n",
     "model.add(Dense(num_classes, activation='softmax'))\n",
-    "model.summary()"
+    "\n",
+    "model.summary()  # wyświetl podsumowanie architektury sieci"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 10,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1035,7 +1036,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 12,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1046,25 +1047,35 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/5\n",
-      "469/469 [==============================] - 11s 23ms/step - loss: 0.2463 - accuracy: 0.9238 - val_loss: 0.1009 - val_accuracy: 0.9690\n",
-      "Epoch 2/5\n",
-      "469/469 [==============================] - 10s 22ms/step - loss: 0.1042 - accuracy: 0.9681 - val_loss: 0.0910 - val_accuracy: 0.9739\n",
-      "Epoch 3/5\n",
-      "469/469 [==============================] - 11s 23ms/step - loss: 0.0774 - accuracy: 0.9762 - val_loss: 0.0843 - val_accuracy: 0.9755\n",
-      "Epoch 4/5\n",
-      "469/469 [==============================] - 11s 24ms/step - loss: 0.0606 - accuracy: 0.9815 - val_loss: 0.0691 - val_accuracy: 0.9818\n",
-      "Epoch 5/5\n",
-      "469/469 [==============================] - 10s 22ms/step - loss: 0.0504 - accuracy: 0.9848 - val_loss: 0.0886 - val_accuracy: 0.9772\n"
+      "Epoch 1/10\n",
+      "469/469 [==============================] - 20s 42ms/step - loss: 0.0957 - accuracy: 0.9708 - val_loss: 0.0824 - val_accuracy: 0.9758\n",
+      "Epoch 2/10\n",
+      "469/469 [==============================] - 20s 43ms/step - loss: 0.0693 - accuracy: 0.9793 - val_loss: 0.0807 - val_accuracy: 0.9772\n",
+      "Epoch 3/10\n",
+      "469/469 [==============================] - 18s 38ms/step - loss: 0.0563 - accuracy: 0.9827 - val_loss: 0.0861 - val_accuracy: 0.9758\n",
+      "Epoch 4/10\n",
+      "469/469 [==============================] - 18s 37ms/step - loss: 0.0485 - accuracy: 0.9857 - val_loss: 0.0829 - val_accuracy: 0.9794\n",
+      "Epoch 5/10\n",
+      "469/469 [==============================] - 19s 41ms/step - loss: 0.0428 - accuracy: 0.9876 - val_loss: 0.0955 - val_accuracy: 0.9766\n",
+      "Epoch 6/10\n",
+      "469/469 [==============================] - 22s 47ms/step - loss: 0.0377 - accuracy: 0.9887 - val_loss: 0.0809 - val_accuracy: 0.9794\n",
+      "Epoch 7/10\n",
+      "469/469 [==============================] - 17s 35ms/step - loss: 0.0338 - accuracy: 0.9904 - val_loss: 0.1028 - val_accuracy: 0.9788\n",
+      "Epoch 8/10\n",
+      "469/469 [==============================] - 17s 36ms/step - loss: 0.0322 - accuracy: 0.9911 - val_loss: 0.0937 - val_accuracy: 0.9815\n",
+      "Epoch 9/10\n",
+      "469/469 [==============================] - 18s 37ms/step - loss: 0.0303 - accuracy: 0.9912 - val_loss: 0.0916 - val_accuracy: 0.9829.0304 - accu\n",
+      "Epoch 10/10\n",
+      "469/469 [==============================] - 16s 34ms/step - loss: 0.0263 - accuracy: 0.9926 - val_loss: 0.0958 - val_accuracy: 0.9812\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<tensorflow.python.keras.callbacks.History at 0x1ed6e0565b0>"
+       "<tensorflow.python.keras.callbacks.History at 0x228eac95ac0>"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1072,13 +1083,13 @@
    "source": [
     "model.compile(loss='categorical_crossentropy', optimizer=keras.optimizers.RMSprop(), metrics=['accuracy'])\n",
     "\n",
-    "model.fit(x_train, y_train, batch_size=128, epochs=5, verbose=1,\n",
+    "model.fit(x_train, y_train, batch_size=128, epochs=10, verbose=1,\n",
     "          validation_data=(x_test, y_test))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1089,8 +1100,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Test loss: 0.08859136700630188\n",
-      "Test accuracy: 0.9771999716758728\n"
+      "Test loss: 0.0757974311709404\n",
+      "Test accuracy: 0.9810000061988831\n"
      ]
     }
    ],
@@ -1109,7 +1120,7 @@
     }
    },
    "source": [
-    "Warstwa _dropout_ to metoda regularyzacji, służy zapobieganiu nadmiernemu dopasowaniu sieci. Polega na tym, że część węzłów sieci jest usuwana w sposób losowy."
+    "Warstwa *dropout* to metoda regularyzacji, służy zapobieganiu nadmiernemu dopasowaniu sieci. Polega na tym, że część węzłów sieci jest usuwana w sposób losowy."
    ]
   },
   {
@@ -1884,7 +1895,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.3"
   },
   "livereveal": {
    "start_slideshow_at": "selected",
diff --git a/wyk/11_Wielowarstwowe_sieci_neuronowe.ipynb b/wyk/11_Wielowarstwowe_sieci_neuronowe.ipynb
index 6f9bfb8..54776ad 100644
--- a/wyk/11_Wielowarstwowe_sieci_neuronowe.ipynb
+++ b/wyk/11_Wielowarstwowe_sieci_neuronowe.ipynb
@@ -37,23 +37,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "notes"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/pawel/.local/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
-      "  from ._conv import register_converters as _register_converters\n",
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "\n",
@@ -62,18 +52,12 @@
     "import numpy as np\n",
     "import random\n",
     "\n",
-    "import keras\n",
-    "from keras.datasets import mnist\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense, Dropout, SimpleRNN, LSTM\n",
-    "from keras.optimizers import Adagrad, Adam, RMSprop, SGD\n",
-    "\n",
     "from IPython.display import YouTubeVideo"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "notes"
@@ -128,37 +112,29 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
     }
    },
-   "source": [
-    "#### Funkcja logistyczna – wykres"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
-      "image/png": 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y7MnxPPv6RL55snFW6uL8UpKkr6M1D+0bzH95z+15eN9gHtwz6D4p4KYirgCA78vKSpXv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\nAAAANqV1xVVVVS8lSSmlntE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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fdda9490fd0>"
+       "<Figure size 1008x504 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
+    "# Wykres funkcji logistycznej\n",
     "plot(lambda x: 1 / (1 + math.exp(-x)))"
    ]
   },
@@ -197,37 +173,29 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
     }
    },
-   "source": [
-    "#### Tangens hiperboliczny – wykres"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
-      "image/png": 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KAABEPGut8sNRtXJXpQalxunXF0zURdMGK9rH91MBiAzEFQAAiGiLt5XrN69u0LIdFRrc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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fdda93e9590>"
+       "<Figure size 1008x504 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
+    "# Wykres funkcji tangensa hiperbolicznego\n",
     "plot(lambda x: math.tanh(x))"
    ]
   },
@@ -262,22 +230,9 @@
    },
    "source": [
     "#### ReLU – zalety\n",
-    "* Mniej podatna na problem zanikającego gradientu (_vanishing gradient_) niż funkcje sigmoidalne, dzięki czemu SGD jest szybciej zbieżna.\n",
+    "* Mniej podatna na problem zanikającego gradientu (*vanishing gradient*) niż funkcje sigmoidalne, dzięki czemu SGD jest szybciej zbieżna.\n",
     "* Prostsze obliczanie gradientu.\n",
-    "* Dzięki zerowaniu ujemnych wartości, wygasza neurony, „rozrzedzając” sieć (_sparsity_), co przyspiesza obliczenia."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "source": [
-    "#### ReLU – wady\n",
-    "* Dla dużych wartości gradient może „eksplodować”.\n",
-    "* „Wygaszanie” neuronów."
+    "* Dzięki zerowaniu ujemnych wartości, wygasza neurony, „rozrzedzając” sieć (*sparsity*), co przyspiesza obliczenia."
    ]
   },
   {
@@ -288,30 +243,35 @@
     }
    },
    "source": [
-    "#### ReLU – wykres"
+    "#### ReLU – wady\n",
+    "* Dla dużych wartości gradient może „eksplodować”.\n",
+    "* „Wygaszanie” neuronów."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "subslide"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fdda936c6d0>"
+       "<Figure size 1008x504 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
+    "# Wykres fukncji ReLU\n",
     "plot(lambda x: max(0, x))"
    ]
   },
@@ -349,37 +309,29 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
     }
    },
-   "source": [
-    "#### Softplus – wykres"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fdde8452e10>"
+       "<Figure size 1008x504 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
+    "# Wykres funkcji softplus\n",
     "plot(lambda x: math.log(1 + math.exp(x)))"
    ]
   },
@@ -417,9 +369,9 @@
    "source": [
     "#### Sposoby na zanikający gradient\n",
     "\n",
-    "* Modyfikacja algorytmu optymalizacji (_RProp_, _RMSProp_)\n",
+    "* Modyfikacja algorytmu optymalizacji (*RProp*, *RMSProp*)\n",
     "* Użycie innej funckji aktywacji (ReLU, softplus)\n",
-    "* Dodanie warstw _dropout_\n",
+    "* Dodanie warstw *dropout*\n",
     "* Nowe architektury (LSTM itp.)\n",
     "* Więcej danych, zwiększenie mocy obliczeniowej"
    ]
@@ -447,13 +399,13 @@
     }
    },
    "source": [
-    "### _Batch gradient descent_\n",
+    "### *Batch gradient descent*\n",
     "\n",
     "* Klasyczna wersja metody gradientu prostego\n",
     "* Obliczamy gradient funkcji kosztu względem całego zbioru treningowego:\n",
     "  $$ \\theta := \\theta - \\alpha \\cdot \\nabla_\\theta J(\\theta) $$\n",
     "* Dlatego może działać bardzo powoli\n",
-    "* Nie można dodawać nowych przykładów na bieżąco w trakcie trenowania modelu (_online learning_)"
+    "* Nie można dodawać nowych przykładów na bieżąco w trakcie trenowania modelu (*online learning*)"
    ]
   },
   {
@@ -464,12 +416,12 @@
     }
    },
    "source": [
-    "### _Stochastic gradient descent_ (SGD)\n",
+    "### *Stochastic gradient descent* (SGD)\n",
     "\n",
     "* Aktualizacja parametrów dla każdego przykładu:\n",
     "  $$ \\theta := \\theta - \\alpha \\cdot \\nabla_\\theta \\, J \\! \\left( \\theta, x^{(i)}, y^{(i)} \\right) $$\n",
     "* Dużo szybszy niż _batch gradient descent_\n",
-    "* Można dodawać nowe przykłady na bieżąco w trakcie trenowania (_online learning_)"
+    "* Można dodawać nowe przykłady na bieżąco w trakcie trenowania (*online learning*)"
    ]
   },
   {
@@ -480,13 +432,20 @@
     }
    },
    "source": [
-    "### _Stochastic gradient descent_ (SGD)\n",
-    "\n",
     "* Częsta aktualizacja parametrów z dużą wariancją:\n",
     "\n",
-    "<img src=\"http://ruder.io/content/images/2016/09/sgd_fluctuation.png\" style=\"margin: auto;\" width=\"50%\" />\n",
-    "\n",
-    "* Z jednej strony dzięki temu nie utyka w złych minimach lokalnych, ale z drugiej strony może „wyskoczyć” z dobrego minimum"
+    "<img src=\"http://ruder.io/content/images/2016/09/sgd_fluctuation.png\" style=\"margin: auto;\" width=\"50%\" />"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "* Z jednej strony dzięki temu uczenie nie \"utyka\" w złych minimach lokalnych, ale z drugiej strony może „wyskoczyć” z dobrego minimum"
    ]
   },
   {
@@ -516,7 +475,7 @@
    "source": [
     "### Wady klasycznej metody gradientu prostego, czyli dlaczego potrzebujemy optymalizacji\n",
     "\n",
-    "* Trudno dobrać właściwą szybkość uczenia (_learning rate_)\n",
+    "* Trudno dobrać właściwą szybkość uczenia (*learning rate*)\n",
     "* Jedna ustalona wartość stałej uczenia się dla wszystkich parametrów\n",
     "* Funkcja kosztu dla sieci neuronowych nie jest wypukła, więc uczenie może utknąć w złym minimum lokalnym lub punkcie siodłowym"
    ]
@@ -565,7 +524,7 @@
     }
    },
    "source": [
-    "### Przyspiesony gradient Nesterova (_Nesterov Accelerated Gradient_, NAG)\n",
+    "### Przyspiesony gradient Nesterova (*Nesterov Accelerated Gradient*, NAG)\n",
     "\n",
     "* Momentum czasami powoduje niekontrolowane rozpędzanie się piłki, przez co staje się „mniej sterowna”\n",
     "* Nesterov do piłki posiadającej pęd dodaje „hamulec”, który spowalnia piłkę przed wzniesieniem:\n",
@@ -584,8 +543,8 @@
     "### Adagrad\n",
     "\n",
     "* “<b>Ada</b>ptive <b>grad</b>ient”\n",
-    "* Adagrad dostosowuje współczynnik uczenia (_learning rate_) do parametrów: zmniejsza go dla cech występujących częściej, a zwiększa dla występujących rzadziej\n",
-    "* Świetny do trenowania na rzadkich (_sparse_) zbiorach danych\n",
+    "* Adagrad dostosowuje współczynnik uczenia (*learning rate*) do parametrów: zmniejsza go dla cech występujących częściej, a zwiększa dla występujących rzadziej\n",
+    "* Świetny do trenowania na rzadkich (*sparse*) zbiorach danych\n",
     "* Wada: współczynnik uczenia może czasami gwałtownie maleć"
    ]
   },
@@ -650,7 +609,7 @@
     }
    },
    "source": [
-    "<img src=\"contours_evaluation_optimizers.gif\" style=\"margin: auto;\" width=\"80%\" />"
+    "<img src=\"contours_evaluation_optimizers.gif\" style=\"margin: auto;\" width=\"60%\" />"
    ]
   },
   {
@@ -661,7 +620,7 @@
     }
    },
    "source": [
-    "<img src=\"saddle_point_evaluation_optimizers.gif\" style=\"margin: auto;\" width=\"80%\" />"
+    "<img src=\"saddle_point_evaluation_optimizers.gif\" style=\"margin: auto;\" width=\"50%\" />"
    ]
   }
  ],