diff --git a/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb b/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb
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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "En2vX4FuwHlu"
+      },
+      "source": [
+        "## Simplest Introduction to Neural Networks with Keras\n",
+        "\n",
+        "> This notebook is a part of [AI for Beginners Curricula](http://github.com/microsoft/ai-for-beginners). Visit the repository for complete set of learning materials.\n",
+        "\n",
+        "### Neural Frameworks\n",
+        "\n",
+        "There are several frameworks for training neural networks. However, if you want to get started fast and not go into much detail on how things work internally - you should consider using [Keras](https://keras.io/). This short tutorial will get you started, and if you want to get deeper into understanding how things work - look into [Introduction to Tensorflow and Keras](IntroKerasTF.ipynb) notebook."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8cACQoFMwHl3"
+      },
+      "source": [
+        "### Getting things ready\n",
+        "\n",
+        "Keras is a part of Tensorflow 2.x framework. Let's make sure we have version 2.x.x of Tensorflow installed:\n",
+        "```\n",
+        "pip install tensorflow\n",
+        "```\n",
+        "or\n",
+        "```\n",
+        "conda install tensorflow\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xwqVx9-bwHl3",
+        "outputId": "2aa591b4-b647-441f-9c8e-4e0da2d517a0",
+        "tags": []
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tensorflow version = 2.7.0\n",
+            "Keras version = 2.7.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "import tensorflow as tf\n",
+        "from tensorflow import keras\n",
+        "import numpy as np\n",
+        "from sklearn.datasets import make_classification\n",
+        "import matplotlib.pyplot as plt\n",
+        "print(f'Tensorflow version = {tf.__version__}')\n",
+        "print(f'Keras version = {keras.__version__}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6tp2xGV7wHl4"
+      },
+      "source": [
+        "## Basic Concepts: Tensor\n",
+        "\n",
+        "**Tensor** is a multi-dimensional array. It is very convenient to use tensors to represent different types of data:\n",
+        "* 400x400 - black-and-white picture\n",
+        "* 400x400x3 - color picture \n",
+        "* 16x400x400x3 - minibatch of 16 color pictures\n",
+        "* 25x400x400x3 - one second of 25-fps video\n",
+        "* 8x25x400x400x3 - minibatch of 8 1-second videos\n",
+        "\n",
+        "Tensors give us a convenient way to represent input/output data, as well we weights inside the neural network."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A10prCPowHl7"
+      },
+      "source": [
+        "## Sample Problem\n",
+        "\n",
+        "Let's consider binary classification problem. A good example of such a problem would be a tumour classification between malignant and benign based on it's size and age. Let's start by generating some sample data:\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "j0OTPkGpwHl7",
+        "scrolled": false,
+        "trusted": true
+      },
+      "outputs": [],
+      "source": [
+        "np.random.seed(0) # pick the seed for reproducibility - change it to explore the effects of random variations\n",
+        "\n",
+        "n = 100\n",
+        "X, Y = make_classification(n_samples = n, n_features=2,\n",
+        "                           n_redundant=0, n_informative=2, flip_y=0.05,class_sep=1.5)\n",
+        "X = X.astype(np.float32)\n",
+        "Y = Y.astype(np.int32)\n",
+        "\n",
+        "split = [ 70*n//100 ]\n",
+        "train_x, test_x = np.split(X, split)\n",
+        "train_labels, test_labels = np.split(Y, split)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "c-_BjSHPwHl8",
+        "scrolled": false,
+        "trusted": true
+      },
+      "outputs": [],
+      "source": [
+        "def plot_dataset(features, labels, W=None, b=None):\n",
+        "    # prepare the plot\n",
+        "    fig, ax = plt.subplots(1, 1)\n",
+        "    ax.set_xlabel('$x_i[0]$ -- (feature 1)')\n",
+        "    ax.set_ylabel('$x_i[1]$ -- (feature 2)')\n",
+        "    colors = ['r' if l else 'b' for l in labels]\n",
+        "    ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)\n",
+        "    if W is not None:\n",
+        "        min_x = min(features[:,0])\n",
+        "        max_x = max(features[:,1])\n",
+        "        min_y = min(features[:,1])*(1-.1)\n",
+        "        max_y = max(features[:,1])*(1+.1)\n",
+        "        cx = np.array([min_x,max_x],dtype=np.float32)\n",
+        "        cy = (0.5-W[0]*cx-b)/W[1]\n",
+        "        ax.plot(cx,cy,'g')\n",
+        "        ax.set_ylim(min_y,max_y)\n",
+        "    fig.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "id": "tq0vFchQwHl8",
+        "outputId": "9a5aa6a0-c92f-4d72-9e78-c0f615804bff",
+        "scrolled": false,
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_103052/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+            "  fig.show()\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plot_dataset(train_x, train_labels)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Normalizing Data\n",
+        "\n",
+        "Before training, it is common to bring our input features to the standard range of [0,1] (or [-1,1]). The exact reasons for that we will discuss later in the course, but in short the reason is the following. We want to avoid values that flow through our network getting too big or too small, and we normally agree to keep all values in the small range close to 0. Thus we initialize the weights with small random numbers, and we keep signals in the same range.\n",
+        "\n",
+        "When normalizing data, we need to subtract min value and divide by range. We compute min value and range using training data, and then normalize test/validation dataset using the same min/range values from the training set. This is because in real life we will only know the training set, and not all incoming new values that the network would be asked to predict. Occasionally, the new value may fall out of the [0,1] range, but that's not crucial.  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "train_x_norm = (train_x-np.min(train_x,axis=0)) / (np.max(train_x,axis=0)-np.min(train_x,axis=0))\n",
+        "test_x_norm = (test_x-np.min(train_x,axis=0)) / (np.max(train_x,axis=0)-np.min(train_x,axis=0))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SjPlpf2-wHl8"
+      },
+      "source": [
+        "## Training One-Layer Network (Perceptron)\n",
+        "\n",
+        "In many cases, a neural network would be a sequence of layers. It can be defined in Keras using `Sequential` model in the following manner:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Model: \"sequential_2\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " dense_2 (Dense)             (None, 1)                 3         \n",
+            "                                                                 \n",
+            " activation_1 (Activation)   (None, 1)                 0         \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 3\n",
+            "Trainable params: 3\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ],
+      "source": [
+        "model = keras.models.Sequential()\n",
+        "model.add(keras.Input(shape=(2,)))\n",
+        "model.add(keras.layers.Dense(1))\n",
+        "model.add(keras.layers.Activation(keras.activations.sigmoid))\n",
+        "\n",
+        "model.summary()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Here, we first create the model, and then add layers to it:\n",
+        "* First `Input` layer (which is not strictly speaking a layer) contains the specification of network's input size\n",
+        "* `Dense` layer is the actual perceptron that contains trainable weights\n",
+        "* Finally, there is a layer with *sigmoid* `Activation` function to bring the result of the network into 0-1 range (to make it a probability).\n",
+        "\n",
+        "Input size, as well as activation function, can also be specified directly in the `Dense` layer for brevity: "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Model: \"sequential_9\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " dense_9 (Dense)             (None, 1)                 3         \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 3\n",
+            "Trainable params: 3\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ],
+      "source": [
+        "model = keras.models.Sequential()\n",
+        "model.add(keras.layers.Dense(1,input_shape=(2,),activation='sigmoid'))\n",
+        "model.summary()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Before training the model, we need to **compile it**, which essentially mean specifying:\n",
+        "* **Loss function**, which defines how loss is calculated. Because we have two-class classification problem, we will use *binary cross-entropy loss*.\n",
+        "* **Optimizer** to use. The simplest option would be to use `sgd` for *stochastic gradient descent*, or you can use more sophisticated optimizers such as `adam`.\n",
+        "* **Metrics** that we want to use to measure success of our training. Since it is classification task, a good metrics would be `Accuracy` (or `acc` for short)\n",
+        "\n",
+        "We can specify loss, metrics and optimizer either as strings, or by providing some objects from Keras framework. In our example, we need to specify `learning_rate` parameter to fine-tune learning speed of our model, and thus we provide full name of Keras SGD optimizer."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "model.compile(optimizer=keras.optimizers.SGD(learning_rate=0.2),loss='binary_crossentropy',metrics=['acc'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "After compiling the model, we can do the actual training by calling `fit` method. The most important parameters are:\n",
+        "* `x` and `y` specify training data, features and labels respectively\n",
+        "* If we want validation to be performed on each epoch, we can specify `validation_data` parameter, which would be a tuple of features and labels\n",
+        "* `epochs` specified the number of epochs\n",
+        "* If we want training to happen in minibatches, we can speficu `batch_size` parameter. You can also pre-batch the data manually before passing it to `x`/`y`/`validation_data`, in which case you do not need `batch_size`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/10\n",
+            "70/70 [==============================] - 0s 4ms/step - loss: 0.3379 - acc: 0.9000 - val_loss: 0.3282 - val_acc: 0.9000\n",
+            "Epoch 2/10\n",
+            "70/70 [==============================] - 0s 2ms/step - loss: 0.3270 - acc: 0.9429 - val_loss: 0.3336 - val_acc: 0.9000\n",
+            "Epoch 3/10\n",
+            "70/70 [==============================] - 0s 2ms/step - loss: 0.3195 - acc: 0.9143 - val_loss: 0.3137 - val_acc: 0.9000\n",
+            "Epoch 4/10\n",
+            "70/70 [==============================] - 0s 2ms/step - loss: 0.3087 - acc: 0.9286 - val_loss: 0.2970 - val_acc: 0.9333\n",
+            "Epoch 5/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.3006 - acc: 0.9429 - val_loss: 0.3210 - val_acc: 0.9000\n",
+            "Epoch 6/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.3003 - acc: 0.9000 - val_loss: 0.2985 - val_acc: 0.9000\n",
+            "Epoch 7/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.2956 - acc: 0.9286 - val_loss: 0.3037 - val_acc: 0.9000\n",
+            "Epoch 8/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.2891 - acc: 0.9429 - val_loss: 0.3035 - val_acc: 0.9000\n",
+            "Epoch 9/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.2809 - acc: 0.9000 - val_loss: 0.2815 - val_acc: 0.9000\n",
+            "Epoch 10/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.2809 - acc: 0.9286 - val_loss: 0.2907 - val_acc: 0.9000\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<keras.callbacks.History at 0x2c420b89910>"
+            ]
+          },
+          "execution_count": 44,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model.fit(x=train_x_norm,y=train_labels,validation_data=(test_x_norm,test_labels),epochs=10,batch_size=1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s4_Atvn5K4K9"
+      },
+      "source": [
+        "You can try to experiment with different training parameters to see how they affect the training:\n",
+        "* Setting `batch_size` to be too large (or not specifying it at all) may result in less stable training, because with low-dimensional data small batch sizes provide more precise direction of the gradient for each specific case\n",
+        "* Too high `learning_rate` may result in overfitting, or in less stable results, while too low learning rate means it will take more epochs to achieve the result\n",
+        "\n",
+        "> Note that you can call `fit` function several times in a row to further train the network. If you want to start training from scratch - you need to re-run the cell with the model definition. \n",
+        "\n",
+        "To make sure our training worked, let's plot the line that separates two classes. Separation line is defined by the equation $W\\times x + b = 0.5$"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "id": "PgRTHttLwHl9",
+        "outputId": "e4407e1b-edf5-48e5-fdc2-da28120a3c6b",
+        "trusted": true
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_103052/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+            "  fig.show()\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plot_dataset(train_x,train_labels,model.layers[0].weights[0],model.layers[0].weights[1])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dvAiaj_JndyP"
+      },
+      "source": [
+        "## Plotting the training graphs\n",
+        "\n",
+        "`fit` function returns `history` object as a result, which can be used to observe loss and metrics on each epoch. In the example below, we will re-start the training with small learning rate, and will observe how the loss and accuracy behave.\n",
+        "\n",
+        "> **Note** that we are using slightly different syntax for defining `Sequential` model. Instead of `add`-ing layers one by one, we can also specify the list of layers right when creating the model in the first place - this is a bit shorter syntax, and you may prefer to use it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/10\n",
+            "70/70 [==============================] - 1s 5ms/step - loss: 0.6600 - acc: 0.6143 - val_loss: 0.6351 - val_acc: 0.8000\n",
+            "Epoch 2/10\n",
+            "70/70 [==============================] - 0s 2ms/step - loss: 0.6384 - acc: 0.7143 - val_loss: 0.6187 - val_acc: 0.8333\n",
+            "Epoch 3/10\n",
+            "70/70 [==============================] - 0s 2ms/step - loss: 0.6188 - acc: 0.7571 - val_loss: 0.6001 - val_acc: 0.8667\n",
+            "Epoch 4/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.6022 - acc: 0.7714 - val_loss: 0.5837 - val_acc: 0.9000\n",
+            "Epoch 5/10\n",
+            "70/70 [==============================] - 0s 2ms/step - loss: 0.5860 - acc: 0.8571 - val_loss: 0.5673 - val_acc: 0.9000\n",
+            "Epoch 6/10\n",
+            "70/70 [==============================] - 0s 2ms/step - loss: 0.5702 - acc: 0.8571 - val_loss: 0.5597 - val_acc: 0.8667\n",
+            "Epoch 7/10\n",
+            "70/70 [==============================] - 0s 2ms/step - loss: 0.5568 - acc: 0.8286 - val_loss: 0.5458 - val_acc: 0.9000\n",
+            "Epoch 8/10\n",
+            "70/70 [==============================] - 0s 2ms/step - loss: 0.5430 - acc: 0.8714 - val_loss: 0.5325 - val_acc: 0.9000\n",
+            "Epoch 9/10\n",
+            "70/70 [==============================] - 0s 2ms/step - loss: 0.5308 - acc: 0.8714 - val_loss: 0.5234 - val_acc: 0.9000\n",
+            "Epoch 10/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.5175 - acc: 0.9143 - val_loss: 0.5170 - val_acc: 0.8667\n"
+          ]
+        }
+      ],
+      "source": [
+        "model = keras.models.Sequential([\n",
+        "    keras.layers.Dense(1,input_shape=(2,),activation='sigmoid')])\n",
+        "model.compile(optimizer=keras.optimizers.SGD(learning_rate=0.05),loss='binary_crossentropy',metrics=['acc'])\n",
+        "hist = model.fit(x=train_x_norm,y=train_labels,validation_data=(test_x_norm,test_labels),epochs=10,batch_size=1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 50,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x2c41a32fe80>]"
+            ]
+          },
+          "execution_count": 50,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.plot(hist.history['acc'])\n",
+        "plt.plot(hist.history['val_acc'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Multi-Class Classification\n",
+        "\n",
+        "If you need to solve a problem of multi-class classification, your network would have more that one output - corresponding to the number of classes $C$. Each output will contain the probability of a given class.\n",
+        "\n",
+        "> Note that you can also use a network with two outputs to perform binary classification in the same manner. That is exactly what we will demonstrate now.\n",
+        "\n",
+        "When you expect a network to output a set of probabilities $p_1,\\dots, p_C$, we need all of them to add up to 1. To ensure this, we use `softmax` as a final activation function on the last layer. **Softmax** takes a vector input, and makes sure that all components of that vector are transformed into probabilities.\n",
+        "\n",
+        "Also, since the output of the network is a $C$-dimensional vector, we need labels to have the same form. This can be achieved by using **one-hot encoding**, when the number of a class $i$ is converted to a vector of zeroes, with 1 at the $i$-th position.\n",
+        "\n",
+        "To compare the probability output of the neural network with expected one-hot-encoded label, we use **cross-entropy loss** function. It takes two probability distributions, and outputs a value of how different they are.\n",
+        "\n",
+        "So, to summarize what we need to do for multi-class classification with $C$ classes:\n",
+        "* The network should have $C$ neurons in the last layer\n",
+        "* Last activation function should be **softmax**\n",
+        "* Loss should be **cross-entropy loss**\n",
+        "* Labels should be converted to **one-hot encoding** (this can be done using `numpy`, or using Keras utils `to_categorical`)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/10\n",
+            "70/70 [==============================] - 1s 6ms/step - loss: 0.6524 - acc: 0.7000 - val_loss: 0.5936 - val_acc: 0.9000\n",
+            "Epoch 2/10\n",
+            "70/70 [==============================] - 0s 2ms/step - loss: 0.5715 - acc: 0.8286 - val_loss: 0.5255 - val_acc: 0.8333\n",
+            "Epoch 3/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.4820 - acc: 0.8714 - val_loss: 0.4213 - val_acc: 0.9000\n",
+            "Epoch 4/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.4426 - acc: 0.9000 - val_loss: 0.3694 - val_acc: 0.9333\n",
+            "Epoch 5/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.3602 - acc: 0.9000 - val_loss: 0.3454 - val_acc: 0.9000\n",
+            "Epoch 6/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.3209 - acc: 0.8857 - val_loss: 0.2862 - val_acc: 0.9333\n",
+            "Epoch 7/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.2905 - acc: 0.9286 - val_loss: 0.2787 - val_acc: 0.9000\n",
+            "Epoch 8/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.2698 - acc: 0.9000 - val_loss: 0.2381 - val_acc: 0.9333\n",
+            "Epoch 9/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.2639 - acc: 0.8857 - val_loss: 0.2217 - val_acc: 0.9667\n",
+            "Epoch 10/10\n",
+            "70/70 [==============================] - 0s 2ms/step - loss: 0.2592 - acc: 0.9286 - val_loss: 0.2391 - val_acc: 0.9000\n"
+          ]
+        }
+      ],
+      "source": [
+        "model = keras.models.Sequential([\n",
+        "    keras.layers.Dense(5,input_shape=(2,),activation='relu'),\n",
+        "    keras.layers.Dense(2,activation='softmax')\n",
+        "])\n",
+        "model.compile(keras.optimizers.Adam(0.01),'categorical_crossentropy',['acc'])\n",
+        "\n",
+        "# Two ways to convert to one-hot encoding\n",
+        "train_labels_onehot = keras.utils.to_categorical(train_labels)\n",
+        "test_labels_onehot = np.eye(2)[test_labels]\n",
+        "\n",
+        "hist = model.fit(x=train_x_norm,y=train_labels_onehot,\n",
+        "                 validation_data=[test_x_norm,test_labels_onehot],batch_size=1,epochs=10)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Sparse Categorical Cross-Entropy\n",
+        "\n",
+        "Often labels in multi-class classification are represented by class numbers. Keras also supports another kind of loss function called **sparse categorical crossentropy**, which expects class number to be integers, and not one-hot vectors. Using this kind of loss function, we can simplify our training code:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 55,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/10\n",
+            "70/70 [==============================] - 1s 6ms/step - loss: 0.2353 - acc: 0.9143 - val_loss: 0.2190 - val_acc: 0.9000\n",
+            "Epoch 2/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.2243 - acc: 0.9286 - val_loss: 0.1886 - val_acc: 0.9333\n",
+            "Epoch 3/10\n",
+            "70/70 [==============================] - 0s 2ms/step - loss: 0.2366 - acc: 0.9143 - val_loss: 0.2262 - val_acc: 0.9000\n",
+            "Epoch 4/10\n",
+            "70/70 [==============================] - 0s 2ms/step - loss: 0.2259 - acc: 0.9429 - val_loss: 0.2124 - val_acc: 0.9000\n",
+            "Epoch 5/10\n",
+            "70/70 [==============================] - 0s 2ms/step - loss: 0.2061 - acc: 0.9429 - val_loss: 0.2691 - val_acc: 0.9000\n",
+            "Epoch 6/10\n",
+            "70/70 [==============================] - 0s 2ms/step - loss: 0.2200 - acc: 0.9286 - val_loss: 0.2344 - val_acc: 0.9000\n",
+            "Epoch 7/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.2133 - acc: 0.9286 - val_loss: 0.1973 - val_acc: 0.9000\n",
+            "Epoch 8/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.2062 - acc: 0.9429 - val_loss: 0.1893 - val_acc: 0.9000\n",
+            "Epoch 9/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.2060 - acc: 0.9571 - val_loss: 0.2719 - val_acc: 0.9000\n",
+            "Epoch 10/10\n",
+            "70/70 [==============================] - 0s 3ms/step - loss: 0.2021 - acc: 0.9571 - val_loss: 0.2293 - val_acc: 0.9000\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<keras.callbacks.History at 0x2c42267de80>"
+            ]
+          },
+          "execution_count": 55,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "model.compile(keras.optimizers.Adam(0.01),'sparse_categorical_crossentropy',['acc'])\n",
+        "model.fit(x=train_x_norm,y=train_labels,validation_data=[test_x_norm,test_labels],batch_size=1,epochs=10)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Multi-Label Classification\n",
+        "\n",
+        "Sometime we have cases when our objects can belong to two classes at once. As an example, suppose we want to develop a classifier for cats and dogs on the picture, but we also want to allow cases when both cats and dogs are present.\n",
+        "\n",
+        "With multi-label classification, instead of one-hot encoded vector, we will have a vector that has 1 in position corresponding to all classes relevant to the input sample. Thus, output of the network should not have normalized probabilities for all classes, but rather for each class individually - which corresponds to using **sigmoid** activation function. Cross-entropy loss can still be used as a loss function.\n",
+        "\n",
+        "> **Note** that this is very similar to using **different neural networks** to do binary classification for each particular class - only the initial part of the network (up to final classification layer) is shared for all classes."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BmHNhUU8bqEX"
+      },
+      "source": [
+        "## Summary of Classification Loss Functions\n",
+        "\n",
+        "We have seen that binary, multi-class and multi-label classification differ by the type of loss function and activation function on the last layer of the network. It may all be a little bit confusing if you are just starting to learn, but here are a few rules to keep in mind:\n",
+        "* If the network has one output (**binary classification**), we use **sigmoid** activation function, for **multiclass classification** - **softmax**\n",
+        "* If the output class is represented as one-hot-encoding, the loss function will be **cross entropy loss** (categorical cross-entropy), if the output contains class number - **sparse categorical cross-entropy**.  For **binary classification** - use **binary cross-entropy** (same as **log loss**)\n",
+        "* **Multi-label classification** is when we can have an object belonging to several classes at the same time. In this case, we need to encode labels using one-hot encoding, and use **sigmoid** as activation function, so that each class probability is between 0 and 1.\n",
+        "\n",
+        "| Classification | Label Format | Activation Function | Loss |\n",
+        "|---------------|-----------------------|-----------------|----------|\n",
+        "| Binary      | Probability of 1st class | sigmoid | binary crossentropy |\n",
+        "| Binary      | One-hot encoding (2 outputs) | softmax | categorical crossentropy |\n",
+        "| Multiclass |  One-hot encoding | softmax | categorical crossentropy |\n",
+        "| Multiclass | Class Number | softmax | sparse categorical crossentropy |\n",
+        "| Multilabel | One-hot encoding | sigmoid | categorical crossentropy |\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gZ-kWx84bMDH"
+      },
+      "source": [
+        "**Task**: \n",
+        "Use Keras to train a classifier for MNIST handwritten digits:\n",
+        "* Notice that Keras contains some standard datasets, including MNIST. To use MNIST from Keras, you only need a couple of lines of code (more information [here](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/mnist))\n",
+        "* Try several network configuration, with different number of layers/neurons, activation functions.\n",
+        "\n",
+        "What is the best accuracy you were able to achieve?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yX6hqiafwHl9"
+      },
+      "source": [
+        "## Takeaways\n",
+        "\n",
+        "* **Keras** is really recommended for beginners, because it allows to construct networks from layers quite easily, and then train it with just a couple of lines of code\n",
+        "* If non-standard architecture is needed, you would need to learn a bit deeper into Tensorflow. Or you can ask someone to implement custom logic as a Keras layer, and then use it in Keras models\n",
+        "* It is a good idea to look at PyTorch as well and compare approaches. \n",
+        "\n",
+        "A good sample notebook from the creator of Keras on Keras and Tensorflow 2.0 can be found [here](https://t.co/k694J95PI8)."
+      ]
+    }
+  ],
+  "metadata": {
+    "celltoolbar": "Slideshow",
+    "colab": {
+      "collapsed_sections": [],
+      "name": "IntroKerasTF.ipynb",
+      "provenance": []
+    },
+    "interpreter": {
+      "hash": "0cb620c6d4b9f7a635928804c26cf22403d89d98d79684e4529119355ee6d5a5"
+    },
+    "kernelspec": {
+      "display_name": "Python 3.8.12 64-bit (conda)",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.12"
+    },
+    "livereveal": {
+      "start_slideshow_at": "selected"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb b/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb
index 588d8652..171a3cf5 100644
--- a/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb
+++ b/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb
@@ -66,7 +66,7 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "2.3.0\n"
+            "2.7.0\n"
           ]
         }
       ],
@@ -123,16 +123,16 @@
             "[[1 2]\n",
             " [3 4]], shape=(2, 2), dtype=int32)\n",
             "tf.Tensor(\n",
-            "[[-0.50882405  1.1335199  -0.78524745]\n",
-            " [ 0.5572137   0.8736796  -0.05827063]\n",
-            " [-0.4501901   0.2969733  -1.660203  ]\n",
-            " [-0.13222459  0.23592396 -0.32563084]\n",
-            " [-0.9020366  -0.24209827  0.72447866]\n",
-            " [ 0.22193347  1.3671101  -0.13040254]\n",
-            " [-0.35891932 -2.0387182   0.7902264 ]\n",
-            " [ 2.590151   -0.40071017 -0.7124725 ]\n",
-            " [ 1.1319299   0.34047043  1.0180026 ]\n",
-            " [ 0.57838875 -1.5080605   0.8308813 ]], shape=(10, 3), dtype=float32)\n"
+            "[[-0.33552304 -1.8252622  -1.8532339 ]\n",
+            " [ 1.0871267  -1.2779568   0.5240014 ]\n",
+            " [-0.12793781 -1.8618349  -0.9020286 ]\n",
+            " [ 0.5948797   0.11144501 -2.0396452 ]\n",
+            " [ 0.47620854  1.1726047  -0.4405675 ]\n",
+            " [-0.27211484 -0.08985762 -0.03376012]\n",
+            " [ 0.64274263  0.53368104 -0.9006528 ]\n",
+            " [-0.43745974 -1.0081122  -0.13442488]\n",
+            " [ 0.36497566  1.3221073  -1.8739727 ]\n",
+            " [ 0.94821155 -0.02817811  1.3563292 ]], shape=(10, 3), dtype=float32)\n"
           ]
         }
       ],
@@ -169,16 +169,16 @@
           "text": [
             "tf.Tensor(\n",
             "[[ 0.          0.          0.        ]\n",
-            " [ 1.0660378  -0.2598403   0.7269768 ]\n",
-            " [ 0.05863395 -0.8365466  -0.87495553]\n",
-            " [ 0.37659946 -0.89759594  0.4596166 ]\n",
-            " [-0.39321256 -1.3756182   1.509726  ]\n",
-            " [ 0.73075753  0.23359025  0.6548449 ]\n",
-            " [ 0.14990473 -3.172238    1.5754738 ]\n",
-            " [ 3.0989752  -1.53423     0.07277495]\n",
-            " [ 1.640754   -0.79304945  1.8032501 ]\n",
-            " [ 1.0872128  -2.6415803   1.6161287 ]], shape=(10, 3), dtype=float32)\n",
-            "[0.60120213 3.1065722  0.45600685]\n"
+            " [ 1.4226497   0.54730535  2.3772354 ]\n",
+            " [ 0.20758523 -0.03657269  0.9512053 ]\n",
+            " [ 0.93040276  1.9367073  -0.18641126]\n",
+            " [ 0.8117316   2.9978669   1.4126664 ]\n",
+            " [ 0.0634082   1.7354046   1.8194739 ]\n",
+            " [ 0.97826564  2.3589432   0.9525811 ]\n",
+            " [-0.1019367   0.81715     1.718809  ]\n",
+            " [ 0.7004987   3.1473694  -0.02073872]\n",
+            " [ 1.2837346   1.7970841   3.2095633 ]], shape=(10, 3), dtype=float32)\n",
+            "[0.71496403 0.16117539 0.15672949]\n"
           ]
         }
       ],
@@ -215,7 +215,7 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "<tf.Variable 'Variable:0' shape=(3,) dtype=float32, numpy=array([ 2.7274222 ,  0.05809033, -0.3086378 ], dtype=float32)>\n"
+            "<tf.Variable 'Variable:0' shape=(3,) dtype=float32, numpy=array([ 2.9411097, -2.9513645, -6.2979555], dtype=float32)>\n"
           ]
         }
       ],
@@ -250,7 +250,7 @@
         {
           "data": {
             "text/plain": [
-              "<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.69976234, -1.5164013 , -0.19107199], dtype=float32)>"
+              "<tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 2.9411097, -2.9513645, -6.2979555], dtype=float32)>"
             ]
           },
           "execution_count": 5,
@@ -279,7 +279,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 6,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -294,8 +294,8 @@
           "output_type": "stream",
           "text": [
             "tf.Tensor(\n",
-            "[[-0.9959172  -0.79481506]\n",
-            " [ 0.74432266  0.7713953 ]], shape=(2, 2), dtype=float32)\n"
+            "[[ 0.40935674 -0.3495818 ]\n",
+            " [ 0.94165146 -0.33209163]], shape=(2, 2), dtype=float32)\n"
           ]
         }
       ],
@@ -324,7 +324,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 7,
       "metadata": {
         "id": "j723455WwHl7",
         "trusted": true
@@ -339,7 +339,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 8,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -352,10 +352,10 @@
         {
           "data": {
             "text/plain": [
-              "<matplotlib.collections.PathCollection at 0x203d1780f40>"
+              "<matplotlib.collections.PathCollection at 0x12892776880>"
             ]
           },
-          "execution_count": 7,
+          "execution_count": 8,
           "metadata": {},
           "output_type": "execute_result"
         },
@@ -397,7 +397,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 9,
       "metadata": {
         "id": "QxhI4GlB6aiH"
       },
@@ -436,7 +436,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": 10,
       "metadata": {
         "id": "-991PErM7fJU"
       },
@@ -464,7 +464,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": 11,
       "metadata": {
         "id": "nOuu0qpx-wAp"
       },
@@ -478,7 +478,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -491,16 +491,16 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Epoch 0: last batch loss = 63.5879\n",
-            "Epoch 1: last batch loss = 6.0149\n",
-            "Epoch 2: last batch loss = 0.9082\n",
-            "Epoch 3: last batch loss = 0.3712\n",
-            "Epoch 4: last batch loss = 0.2928\n",
-            "Epoch 5: last batch loss = 0.2765\n",
-            "Epoch 6: last batch loss = 0.2724\n",
-            "Epoch 7: last batch loss = 0.2712\n",
-            "Epoch 8: last batch loss = 0.2709\n",
-            "Epoch 9: last batch loss = 0.2708\n"
+            "Epoch 0: last batch loss = 94.5247\n",
+            "Epoch 1: last batch loss = 9.3428\n",
+            "Epoch 2: last batch loss = 1.4166\n",
+            "Epoch 3: last batch loss = 0.5224\n",
+            "Epoch 4: last batch loss = 0.3807\n",
+            "Epoch 5: last batch loss = 0.3495\n",
+            "Epoch 6: last batch loss = 0.3413\n",
+            "Epoch 7: last batch loss = 0.3390\n",
+            "Epoch 8: last batch loss = 0.3384\n",
+            "Epoch 9: last batch loss = 0.3382\n"
           ]
         }
       ],
@@ -521,7 +521,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
+      "execution_count": 13,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -533,11 +533,11 @@
         {
           "data": {
             "text/plain": [
-              "(<tf.Variable 'Variable:0' shape=(1, 1) dtype=float32, numpy=array([[2.00624]], dtype=float32)>,\n",
-              " <tf.Variable 'Variable:0' shape=(1,) dtype=float32, numpy=array([1.0982299], dtype=float32)>)"
+              "(<tf.Variable 'Variable:0' shape=(1, 1) dtype=float32, numpy=array([[1.8616779]], dtype=float32)>,\n",
+              " <tf.Variable 'Variable:0' shape=(1,) dtype=float32, numpy=array([1.0710956], dtype=float32)>)"
             ]
           },
-          "execution_count": 14,
+          "execution_count": 13,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -548,7 +548,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": 14,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -561,16 +561,16 @@
         {
           "data": {
             "text/plain": [
-              "[<matplotlib.lines.Line2D at 0x203d1cfa340>]"
+              "[<matplotlib.lines.Line2D at 0x12892ae5eb0>]"
             ]
           },
-          "execution_count": 15,
+          "execution_count": 14,
           "metadata": {},
           "output_type": "execute_result"
         },
         {
           "data": {
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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
@@ -603,7 +603,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 16,
+      "execution_count": 15,
       "metadata": {
         "id": "HK7HPLz3Hyrl"
       },
@@ -636,7 +636,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 17,
+      "execution_count": 16,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -658,7 +658,7 @@
             "Epoch 6: last batch loss = 2.9458\n",
             "Epoch 7: last batch loss = 2.7311\n",
             "Epoch 8: last batch loss = 2.5332\n",
-            "Epoch 9: last batch loss = 2.3507\n"
+            "Epoch 9: last batch loss = 2.3508\n"
           ]
         }
       ],
@@ -691,7 +691,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 96,
+      "execution_count": 40,
       "metadata": {
         "id": "j0OTPkGpwHl7",
         "scrolled": false,
@@ -703,7 +703,7 @@
         "\n",
         "n = 100\n",
         "X, Y = make_classification(n_samples = n, n_features=2,\n",
-        "                           n_redundant=0, n_informative=2, flip_y=0.1,class_sep=1)\n",
+        "                           n_redundant=0, n_informative=2, flip_y=0.05,class_sep=1.5)\n",
         "X = X.astype(np.float32)\n",
         "Y = Y.astype(np.int32)\n",
         "\n",
@@ -714,7 +714,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 97,
+      "execution_count": 41,
       "metadata": {
         "id": "c-_BjSHPwHl8",
         "scrolled": false,
@@ -743,7 +743,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 98,
+      "execution_count": 42,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -759,13 +759,13 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_66332/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+            "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_66184/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
             "  fig.show()\n"
           ]
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
@@ -793,13 +793,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 99,
+      "execution_count": 43,
       "metadata": {},
       "outputs": [],
       "source": [
-        "train_x_norm = train_x-np.min(train_x) / (np.max(train_x)-np.min(train_x))\n",
-        "valid_x_norm = valid_x-np.min(train_x) / (np.max(train_x)-np.min(train_x))\n",
-        "test_x_norm = test_x-np.min(train_x) / (np.max(train_x)-np.min(train_x))"
+        "train_x_norm = (train_x-np.min(train_x)) / (np.max(train_x)-np.min(train_x))\n",
+        "valid_x_norm = (valid_x-np.min(train_x)) / (np.max(train_x)-np.min(train_x))\n",
+        "test_x_norm = (test_x-np.min(train_x)) / (np.max(train_x)-np.min(train_x))"
       ]
     },
     {
@@ -823,7 +823,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 112,
+      "execution_count": 52,
       "metadata": {
         "id": "kdDxWeCqwHl8",
         "trusted": true
@@ -833,7 +833,7 @@
         "W = tf.Variable(tf.random.normal(shape=(2,1)),dtype=tf.float32)\n",
         "b = tf.Variable(tf.zeros(shape=(1,),dtype=tf.float32))\n",
         "\n",
-        "learning_rate = 0.01\n",
+        "learning_rate = 0.1\n",
         "\n",
         "@tf.function\n",
         "def train_on_batch(x, y):\n",
@@ -857,7 +857,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 113,
+      "execution_count": 59,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -871,23 +871,23 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Epoch 0: last batch loss = 1.4609\n",
-            "Epoch 1: last batch loss = 1.4618\n",
-            "Epoch 2: last batch loss = 1.2848\n",
-            "Epoch 3: last batch loss = 0.6062\n",
-            "Epoch 4: last batch loss = 0.3228\n",
-            "Epoch 5: last batch loss = 0.3865\n",
-            "Epoch 6: last batch loss = 0.3459\n",
-            "Epoch 7: last batch loss = 0.4791\n",
-            "Epoch 8: last batch loss = 0.9512\n",
-            "Epoch 9: last batch loss = 0.3132\n"
+            "Epoch 0: last batch loss = 0.3823\n",
+            "Epoch 1: last batch loss = 0.5243\n",
+            "Epoch 2: last batch loss = 0.4510\n",
+            "Epoch 3: last batch loss = 0.3261\n",
+            "Epoch 4: last batch loss = 0.4177\n",
+            "Epoch 5: last batch loss = 0.3323\n",
+            "Epoch 6: last batch loss = 0.6294\n",
+            "Epoch 7: last batch loss = 0.6334\n",
+            "Epoch 8: last batch loss = 0.2571\n",
+            "Epoch 9: last batch loss = 0.3425\n"
           ]
         }
       ],
       "source": [
         "# Create a tf.data.Dataset object for easy batched iteration\n",
         "dataset = tf.data.Dataset.from_tensor_slices((train_x_norm.astype(np.float32), train_labels.astype(np.float32)))\n",
-        "dataset = dataset.shuffle(128).batch(4)\n",
+        "dataset = dataset.shuffle(128).batch(2)\n",
         "\n",
         "for epoch in range(10):\n",
         "  for step, (x, y) in enumerate(dataset):\n",
@@ -906,7 +906,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 114,
+      "execution_count": 60,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -921,13 +921,13 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_66332/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+            "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_66184/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
             "  fig.show()\n"
           ]
         },
         {
           "data": {
-            "image/png": 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",
+            "image/png": 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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
@@ -951,7 +951,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 65,
+      "execution_count": 61,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -964,16 +964,16 @@
         {
           "data": {
             "text/plain": [
-              "<matplotlib.collections.PathCollection at 0x203d1e67160>"
+              "<matplotlib.collections.PathCollection at 0x12892a01460>"
             ]
           },
-          "execution_count": 65,
+          "execution_count": 61,
           "metadata": {},
           "output_type": "execute_result"
         },
         {
           "data": {
-            "image/png": 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",
+            "image/png": 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",
             "text/plain": [
               "<Figure size 432x288 with 2 Axes>"
             ]
@@ -985,10 +985,10 @@
         }
       ],
       "source": [
-        "pred = tf.matmul(valid_x,W)+b\n",
+        "pred = tf.matmul(test_x,W)+b\n",
         "fig,ax = plt.subplots(1,2)\n",
-        "ax[0].scatter(valid_x[:,0],valid_x[:,1],c=pred[:,0]>0.5)\n",
-        "ax[1].scatter(valid_x[:,0],valid_x[:,1],c=valid_labels)"
+        "ax[0].scatter(test_x[:,0],test_x[:,1],c=pred[:,0]>0.5)\n",
+        "ax[1].scatter(test_x[:,0],test_x[:,1],c=valid_labels)"
       ]
     },
     {
@@ -1000,7 +1000,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 66,
+      "execution_count": 62,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1012,16 +1012,16 @@
         {
           "data": {
             "text/plain": [
-              "<tf.Tensor: shape=(), dtype=float32, numpy=0.4>"
+              "<tf.Tensor: shape=(), dtype=float32, numpy=0.46666667>"
             ]
           },
-          "execution_count": 66,
+          "execution_count": 62,
           "metadata": {},
           "output_type": "execute_result"
         }
       ],
       "source": [
-        "tf.reduce_mean(tf.cast(((pred[0]>0.5)==valid_labels),tf.float32))"
+        "tf.reduce_mean(tf.cast(((pred[0]>0.5)==test_labels),tf.float32))"
       ]
     },
     {
@@ -1049,7 +1049,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 70,
+      "execution_count": 63,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1062,26 +1062,26 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Epoch 0: last batch loss = 9.8168, acc = 0.1667\n",
-            "Epoch 1: last batch loss = 9.3657, acc = 0.1667\n",
-            "Epoch 2: last batch loss = 4.4084, acc = 0.8333\n",
-            "Epoch 3: last batch loss = 6.0470, acc = 0.8333\n",
-            "Epoch 4: last batch loss = 9.8649, acc = 0.3333\n",
-            "Epoch 5: last batch loss = 6.8309, acc = 0.5000\n",
-            "Epoch 6: last batch loss = 4.8908, acc = 0.8333\n",
-            "Epoch 7: last batch loss = 11.3577, acc = 0.1667\n",
-            "Epoch 8: last batch loss = 9.0503, acc = 0.5000\n",
-            "Epoch 9: last batch loss = 5.9413, acc = 0.6667\n",
-            "Epoch 10: last batch loss = 4.5841, acc = 0.6667\n",
-            "Epoch 11: last batch loss = 5.6880, acc = 0.8333\n",
-            "Epoch 12: last batch loss = 6.4425, acc = 0.6667\n",
-            "Epoch 13: last batch loss = 7.7689, acc = 0.6667\n",
-            "Epoch 14: last batch loss = 8.0344, acc = 0.5000\n",
-            "Epoch 15: last batch loss = 6.3421, acc = 0.6667\n",
-            "Epoch 16: last batch loss = 5.5468, acc = 0.8333\n",
-            "Epoch 17: last batch loss = 5.4036, acc = 0.5000\n",
-            "Epoch 18: last batch loss = 9.8040, acc = 0.1667\n",
-            "Epoch 19: last batch loss = 5.6883, acc = 0.6667\n"
+            "Epoch 0: last batch loss = 4.7787, acc = 1.0000\n",
+            "Epoch 1: last batch loss = 8.4343, acc = 0.5000\n",
+            "Epoch 2: last batch loss = 8.3255, acc = 0.5000\n",
+            "Epoch 3: last batch loss = 7.5579, acc = 0.5000\n",
+            "Epoch 4: last batch loss = 6.5254, acc = 0.5000\n",
+            "Epoch 5: last batch loss = 7.3800, acc = 0.5000\n",
+            "Epoch 6: last batch loss = 7.7586, acc = 0.5000\n",
+            "Epoch 7: last batch loss = 10.4724, acc = 0.0000\n",
+            "Epoch 8: last batch loss = 9.4423, acc = 0.5000\n",
+            "Epoch 9: last batch loss = 4.1888, acc = 1.0000\n",
+            "Epoch 10: last batch loss = 11.2127, acc = 0.0000\n",
+            "Epoch 11: last batch loss = 9.0417, acc = 0.5000\n",
+            "Epoch 12: last batch loss = 7.9847, acc = 0.5000\n",
+            "Epoch 13: last batch loss = 3.7879, acc = 1.0000\n",
+            "Epoch 14: last batch loss = 6.8455, acc = 0.5000\n",
+            "Epoch 15: last batch loss = 6.5204, acc = 0.5000\n",
+            "Epoch 16: last batch loss = 9.2386, acc = 0.5000\n",
+            "Epoch 17: last batch loss = 6.2447, acc = 0.5000\n",
+            "Epoch 18: last batch loss = 3.9107, acc = 1.0000\n",
+            "Epoch 19: last batch loss = 5.7645, acc = 1.0000\n"
           ]
         }
       ],
@@ -1151,7 +1151,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": 64,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1164,48 +1164,49 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Model: \"functional_1\"\n",
+            "Model: \"model\"\n",
             "_________________________________________________________________\n",
-            "Layer (type)                 Output Shape              Param #   \n",
+            " Layer (type)                Output Shape              Param #   \n",
             "=================================================================\n",
-            "input_1 (InputLayer)         [(None, 2)]               0         \n",
-            "_________________________________________________________________\n",
-            "dense (Dense)                (None, 1)                 3         \n",
+            " input_1 (InputLayer)        [(None, 2)]               0         \n",
+            "                                                                 \n",
+            " dense (Dense)               (None, 1)                 3         \n",
+            "                                                                 \n",
             "=================================================================\n",
             "Total params: 3\n",
             "Trainable params: 3\n",
             "Non-trainable params: 0\n",
             "_________________________________________________________________\n",
             "Epoch 1/15\n",
-            "9/9 [==============================] - 0s 5ms/step - loss: 1.2681 - accuracy: 0.1857\n",
+            "9/9 [==============================] - 1s 2ms/step - loss: 0.7812 - accuracy: 0.2857\n",
             "Epoch 2/15\n",
-            "9/9 [==============================] - 0s 1ms/step - loss: 0.7420 - accuracy: 0.5286\n",
+            "9/9 [==============================] - 0s 2ms/step - loss: 0.7142 - accuracy: 0.4000\n",
             "Epoch 3/15\n",
-            "9/9 [==============================] - 0s 2ms/step - loss: 0.5167 - accuracy: 0.7714\n",
+            "9/9 [==============================] - 0s 2ms/step - loss: 0.6683 - accuracy: 0.6143\n",
             "Epoch 4/15\n",
-            "9/9 [==============================] - 0s 1ms/step - loss: 0.4352 - accuracy: 0.8429\n",
+            "9/9 [==============================] - 0s 2ms/step - loss: 0.6221 - accuracy: 0.8429\n",
             "Epoch 5/15\n",
-            "9/9 [==============================] - 0s 2ms/step - loss: 0.4198 - accuracy: 0.8571\n",
+            "9/9 [==============================] - 0s 2ms/step - loss: 0.5843 - accuracy: 0.8857\n",
             "Epoch 6/15\n",
-            "9/9 [==============================] - 0s 2ms/step - loss: 0.4091 - accuracy: 0.8429\n",
+            "9/9 [==============================] - 0s 2ms/step - loss: 0.5447 - accuracy: 0.9429\n",
             "Epoch 7/15\n",
-            "9/9 [==============================] - 0s 1ms/step - loss: 0.4118 - accuracy: 0.8286\n",
+            "9/9 [==============================] - 0s 2ms/step - loss: 0.5135 - accuracy: 0.9286\n",
             "Epoch 8/15\n",
-            "9/9 [==============================] - 0s 1ms/step - loss: 0.4058 - accuracy: 0.8429\n",
+            "9/9 [==============================] - 0s 2ms/step - loss: 0.4878 - accuracy: 0.9429\n",
             "Epoch 9/15\n",
-            "9/9 [==============================] - 0s 1ms/step - loss: 0.4072 - accuracy: 0.8571\n",
+            "9/9 [==============================] - 0s 2ms/step - loss: 0.4679 - accuracy: 0.9429\n",
             "Epoch 10/15\n",
-            "9/9 [==============================] - 0s 1ms/step - loss: 0.4125 - accuracy: 0.8714\n",
+            "9/9 [==============================] - 0s 2ms/step - loss: 0.4446 - accuracy: 0.9429\n",
             "Epoch 11/15\n",
-            "9/9 [==============================] - 0s 1ms/step - loss: 0.4186 - accuracy: 0.8429\n",
+            "9/9 [==============================] - 0s 2ms/step - loss: 0.4349 - accuracy: 0.8714\n",
             "Epoch 12/15\n",
-            "9/9 [==============================] - 0s 1ms/step - loss: 0.4092 - accuracy: 0.8429\n",
+            "9/9 [==============================] - 0s 2ms/step - loss: 0.4156 - accuracy: 0.9286\n",
             "Epoch 13/15\n",
-            "9/9 [==============================] - 0s 2ms/step - loss: 0.4078 - accuracy: 0.8429\n",
+            "9/9 [==============================] - 0s 2ms/step - loss: 0.4019 - accuracy: 0.9429\n",
             "Epoch 14/15\n",
-            "9/9 [==============================] - 0s 1ms/step - loss: 0.4095 - accuracy: 0.8429\n",
+            "9/9 [==============================] - 0s 2ms/step - loss: 0.3908 - accuracy: 0.9286\n",
             "Epoch 15/15\n",
-            "9/9 [==============================] - 0s 1ms/step - loss: 0.4087 - accuracy: 0.8429\n"
+            "9/9 [==============================] - 0s 2ms/step - loss: 0.3777 - accuracy: 0.9286\n"
           ]
         }
       ],
@@ -1221,7 +1222,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 27,
+      "execution_count": 65,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1234,16 +1235,16 @@
         {
           "data": {
             "text/plain": [
-              "[<matplotlib.lines.Line2D at 0x25c76445310>]"
+              "[<matplotlib.lines.Line2D at 0x12894b95250>]"
             ]
           },
-          "execution_count": 27,
+          "execution_count": 65,
           "metadata": {},
           "output_type": "execute_result"
         },
         {
           "data": {
-            "image/png": 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",
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4lh8q5wNjB5HVOz3oKCKSAFTocap4fw07K4/pVLkiEjUVepzKD1UAMG+Kxs9FJDoq9DiVHypn1sj+DMvqGXQUEUkQKvQ4VHrkBAUlVboQtIicFRV6HFoTeu9ScxpuEZHoqdDjUH6ogvFD+jIuu2/QUUQkgajQ48zhY3X877uHNLtFRM6aCj3OvLK5QpeaE5FzokKPM/mhCkZk9WS6LjUnImdJhR5Hjtc18Pr2SuZPHUbT1fxERKKnQo8jr22tpLYhrOmKInJOVOhxJD9UzoDe6cwZrUvNicjZU6HHibqGMK9u2c/cyUNJ06XmROQcqDnixJ93HqRGl5oTkfOgQo8T+aFyemekcnne4KCjiEiCUqHHgXDYeXlTBVdOyKZnui41JyLnRoUeBzaUHGF/jS41JyLnR4UeB/JD5aSlGB+aNCToKCKSwKIqdDNbYGZbzazYzJa2cvtXzWxD5KvIzBrNTHPvouDurAlVcMm4QWT10qXmROTcdVjoZpYKPApcB0wBbjGzKc23cfcH3X2Wu88Cvg685u6HOiFv0tm+/yi7DhzTcIuInLdo9tDnAMXuvtPd64DlwKJ2tr8FeDoW4bqD/KJyzGD+FB0dKiLnJ5pCzwH2Nlsuiaw7g5n1BhYAv23j9iVmts7M1lVWVp5t1qSUv6mcC0f2Z0imLjUnIucnmkJv7SxR3sa2HwbeaGu4xd0fc/fZ7j47Ozs72oxJq+TwcYr2VTNfwy0iEgPRFHoJMLLZci5Q2sa2i9FwS9TWhCoAXWpORGIjmkJfC+SZ2Rgzy6CptFe03MjMsoArgedjGzF55YfKmTC0L2MG9wk6iogkgQ4L3d0bgLuAfGAz8Bt3D5nZnWZ2Z7NNPwKscfdjnRM1uRw8Wsvadw9p71xEYiYtmo3cfTWwusW6ZS2WnwCeiFWwZPfq5v2EXcMtIhI7OlI0IPmhcnL692LqiMygo4hIklChB+BobQOvFx9g/tShutSciMSMCj0Ar22tpK4hrOEWEYkpFXoA8kPlDOyTwft1qTkRiSEVeherawjzP1v2c83kIaSmaLhFRGJHhd7F/rTjADW1utSciMSeCr2L5Ycq6JORymXjdak5EYktFXoXeu9Sc1dNHKJLzYlIzKnQu9A7ew9z4Ggt86fqVLkiEnsq9C6UH6ogPVWXmhORzqFC7yLuTn6onEvHDSazpy41JyKxp0LvIlsrath98Lhmt4hIp1Ghd5H8ogrMYJ4uNScinUSF3kXyQ+VcNGoA2f16BB1FRJKUCr0L7D10nE1l1RpuEZFOpULvAvmhcgBNVxSRTqVC7wJrQhVMGtaPCwbpUnMi0nlU6J3swNFa1u4+xHwNt4hIJ1Ohd7JXNlXgDtdquEVEOpkKvZPlh8rJHdCLKcN1qTkR6Vwq9E5UdaKePxYfYMHUYbrUnIh0uqgK3cwWmNlWMys2s6VtbHOVmW0ws5CZvRbbmInp5U0V1Dc6C2eOCDqKiHQDaR1tYGapwKPAPKAEWGtmK9x9U7Nt+gM/Bxa4+x4z09mngJUFpeQO6MXM3Kygo4hINxDNHvocoNjdd7p7HbAcWNRim1uBZ919D4C7749tzMRz5Hgdf9x+gBtmDNdwi4h0iWgKPQfY22y5JLKuuQnAADP7vZmtN7PPtHZHZrbEzNaZ2brKyspzS5wg1oQqaAg7C6druEVEukY0hd7a7qW3WE4DLgJuAK4FvmlmE874IffH3H22u8/Ozs4+67CJ5IWCUkYN7M20HM1uEZGuEU2hlwAjmy3nAqWtbPOSux9z9wPAH4CZsYmYeA4dq+NPOw6yUMMtItKFoin0tUCemY0xswxgMbCixTbPA5ebWZqZ9QYuBjbHNmriyA+V0xh2bpgxPOgoItKNdDjLxd0bzOwuIB9IBR5395CZ3Rm5fZm7bzazl4ACIAz80t2LOjN4PFtZUMqYwX10MJGIdKkOCx3A3VcDq1usW9Zi+UHgwdhFS0wHjtby5o6DfPFD4zXcIiJdSkeKxthLReWEHQ23iEiXU6HH2MqCUsZl92Hi0H5BRxGRbkaFHkP7a07y1q5DLJwxQsMtItLlVOgx9GJhOe6wUMMtIhIAFXoMrSooY+LQfuRpuEVEAqBCj5HyqpOs3X1IH4aKSGBU6DGyurAM1+wWEQmQCj1GVhWWMXl4JuOy+wYdRUS6KRV6DJQeOcH63Yf1YaiIBEqFHgOrC8sAuGG6Cl1EgqNCj4GVBWVMy8lk9OA+QUcRkW5MhX6e9h46zoa9R7hBF7IQkYCp0M+ThltEJF6o0M/TqsIyZuZmMWpQ76CjiEg3p0I/D3sOHqegpEpzz0UkLqjQz8PKwqYr8V2v4RYRiQMq9POwqqCMC0f1J3eAhltEJHgq9HO068AxQqXV+jBUROKGCv0crSpoGm7R+LmIxAsV+jlaWVDG7AsGMDyrV9BRREQAFfo5Kd5/lC3lNdo7F5G4ElWhm9kCM9tqZsVmtrSV268ysyoz2xD5+lbso8aPVQVlmGl2i4jEl7SONjCzVOBRYB5QAqw1sxXuvqnFpq+7+8JOyBh3VhWW8v7RAxma2TPoKCIip0Szhz4HKHb3ne5eBywHFnVurPi1raKGbRVHdapcEYk70RR6DrC32XJJZF1Ll5jZRjN70cymtnZHZrbEzNaZ2brKyspziBu8lQVlpBgsmDYs6CgiIqeJptCtlXXeYvlt4AJ3nwn8DPjv1u7I3R9z99nuPjs7O/usgsYDd2dVQSkXjxnEkH4abhGR+BJNoZcAI5st5wKlzTdw92p3Pxr5fjWQbmaDY5YyTmwpr2FH5THNbhGRuBRNoa8F8sxsjJllAIuBFc03MLNhZmaR7+dE7vdgrMMGbVVkuOU6DbeISBzqcJaLuzeY2V1APpAKPO7uITO7M3L7MuBjwBfMrAE4ASx295bDMgnN3VlVWMal4wYzqG+PoOOIiJyhw0KHU8Moq1usW9bs+0eAR2IbLb6ESqvZdeAYS64YG3QUEZFW6UjRKK0qLCM1xVgwVcMtIhKfVOhRaJrdUsZl4wczoE9G0HFERFqlQo9C4b4q9hw6roOJRCSuqdCjsKqgjPRU49opGm4RkfilQu+Au7OyoIwPjh9MVu/0oOOIiLRJhd6BDXuPsO/ICRbOGBF0FBGRdqnQO7CqoIyM1BSumTI06CgiIu1SobcjHHZWF5ZxxYTBZPXScIuIxDcVejve2XuY0qqTGm4RkYSgQm/HyoIyMtJSmDt5SNBRREQ6pEJvw3vDLVdNyKZfTw23iEj8U6G3Yd3uw1RU17JwpoZbRCQxqNDbsKqglB5pKcydpOEWEUkMKvRWNIad1UXlXD1pCH16RHVCShGRwKnQW/G/uw5RWVOr2S0iklBU6K1YVVhKr/RUPjQp8a57KiLdlwq9hYbGMC8WljN38hB6Z2i4RUQShwq9hbd2HeLgsTqdKldEEo4KvYWVBWX0yUjlqoma3SIiiUWF3kx9Y5iXisq4ZspQeqanBh1HROSsqNCbeXPHQQ4fr+eG6RpuEZHEE1Whm9kCM9tqZsVmtrSd7d5vZo1m9rHYRew6qwrK6NcjjSsmaHaLiCSeDgvdzFKBR4HrgCnALWY2pY3tHgDyYx2yK9Q1hHkpVM48DbeISIKKZg99DlDs7jvdvQ5YDixqZbsvAb8F9scwX5d5Y8cBqk7Uc4Nmt4hIgoqm0HOAvc2WSyLrTjGzHOAjwLLYRes67s4Tb7xLZs80Ppg3OOg4IiLnJJpCt1bWeYvlnwJfc/fGdu/IbImZrTOzdZWVlVFG7HwrC8p4bVslX75mAj3SNNwiIokpmkMhS4CRzZZzgdIW28wGlpsZwGDgejNrcPf/br6Ruz8GPAYwe/bsln8UAlF1vJ7vvhBiRm4Wt106Oug4IiLnLJpCXwvkmdkYYB+wGLi1+QbuPua9783sCWBlyzKPV/e/tJnDx+t54rNzSE1p7c2IiEhi6LDQ3b3BzO6iafZKKvC4u4fM7M7I7Qk5bg7w1s6DPP2/e7njirFMy8kKOo6IyHmJ6uxT7r4aWN1iXatF7u5/df6xOl9tQyNff66QkQN7cc81eUHHERE5b932dII//58d7Kw8xr9/bo7OqigiSaFbHvpfvL+Gn/++mJtmjeBKHRUqIkmi2xV6OOx8/dlC+vRI4x8WnnHAq4hIwup2hb587V7WvnuYe6+fzOC+PYKOIyISM92q0PdXn+S+FzdzydhBfOyi3KDjiIjEVLcq9O++sInahjA//Oh0IgdBiYgkjW5T6K9sqmBVYRl3Xz2eMYP7BB1HRCTmukWhH6tt4FvPFzFhaF+WXDEu6DgiIp2iW0zA/qc12yirPskzt15KRlq3+BsmIt1Q0rfbxr1HeOJPu/jUxRdw0QUDgo4jItJpkrrQ6xvDLH22kOx+PfjqgolBxxER6VRJPeTy+B93sbmsmmWfuojMnulBxxER6VRJu4e+5+BxHnplG/OnDGXBtGFBxxER6XRJWejuzr3/XUhaSgrfXTQ16DgiIl0iKQv9+Q2lvL79AF+9diLDs3oFHUdEpEskXaEfPlbH91ZuYtbI/nzqAxcEHUdEpMskXaH/4+rNVJ+o576PTtcl5USkW0mqQv9T8QGeWV/CkivGMnl4ZtBxRES6VNIU+sn6Rr7xXCEXDOrN3XN1STkR6X6SZh76I78r5t2Dx3nq9ovpmZ4adBwRkS6XFHvoW8trWPbaDm5+Xy6XjR8cdBwRkUBEVehmtsDMtppZsZktbeX2RWZWYGYbzGydmX0w9lFbFw47S58tILNXOvfeMLmrHlZEJO50OORiZqnAo8A8oARYa2Yr3H1Ts81eBVa4u5vZDOA3wKTOCNzSU2/t5p09R3joEzMZ2CejKx5SRCQuRbOHPgcodved7l4HLAcWNd/A3Y+6u0cW+wBOFyivOskDL23l8rzB3DQrpyseUkQkbkVT6DnA3mbLJZF1pzGzj5jZFmAV8LnYxGvft1cUUd8Y5gc3TdMl5USk24um0FtryjP2wN39OXefBNwEfL/VOzJbEhljX1dZWXlWQVt6qaic/FAFX75mAhcM0iXlRESiKfQSYGSz5VygtK2N3f0PwDgzO2O6ibs/5u6z3X12dnb2WYd9T83Jer69oohJw/px++Vjzvl+RESSSTSFvhbIM7MxZpYBLAZWNN/AzMZbZMzDzN4HZAAHYx32PQ/mb2V/TS333zyD9NSkmHkpInLeOpzl4u4NZnYXkA+kAo+7e8jM7ozcvgy4GfiMmdUDJ4BPNPuQNKbW7z7Mk3/ezW2XjGbWyP6d8RAiIgkpqiNF3X01sLrFumXNvn8AeCC20VqXlmJcnpfNV67VJeVERJpLuEP/Z47sz68+NyfoGCIicUcD0CIiSUKFLiKSJFToIiJJQoUuIpIkVOgiIklChS4ikiRU6CIiSUKFLiKSJKyTjtDv+IHNKoHd5/jjg4EDMYzT2RIpbyJlhcTKm0hZIbHyJlJWOL+8F7h7q2c3DKzQz4eZrXP32UHniFYi5U2krJBYeRMpKyRW3kTKCp2XV0MuIiJJQoUuIpIkErXQHws6wFlKpLyJlBUSK28iZYXEyptIWaGT8ibkGLqIiJwpUffQRUSkBRW6iEiSSLhCN7MFZrbVzIrNbGnQedpiZiPN7H/MbLOZhczsnqAzRcPMUs3sHTNbGXSW9phZfzN7xsy2RP6PLwk6U3vM7G8iz4MiM3vazHoGnak5M3vczPabWVGzdQPN7GUz2x75d0CQGd/TRtYHI8+FAjN7zsz6BxjxNK3lbXbbV8zMzWxwLB4roQrdzFKBR4HrgCnALWY2JdhUbWoA/s7dJwMfAL4Yx1mbuwfYHHSIKDwMvOTuk4CZxHFmM8sB7gZmu/s0mq7NuzjYVGd4AljQYt1S4FV3zwNejSzHgyc4M+vLwDR3nwFsA77e1aHa8QRn5sXMRgLzgD2xeqCEKnRgDlDs7jvdvQ5YDiwKOFOr3L3M3d+OfF9DU+HkBJuqfWaWC9wA/DLoLO0xs0zgCuBfAdy9zt2PBBqqY2lALzNLA3oDpQHnOY27/wE41GL1IuDfI9//O3BTV2ZqS2tZ3X2NuzdEFv8M5HZ5sDa08X8L8BDw90DMZqYkWqHnAHubLZcQ5yUJYGajgQuBtwKO0pGf0vQECwecoyNjgUrg3yLDQ780sz5Bh2qLu+8DfkzTnlgZUOXua4JNFZWh7l4GTTsowJCA80Trc8CLQYdoj5ndCOxz942xvN9EK3RrZV1cz7s0s77Ab4Evu3t10HnaYmYLgf3uvj7oLFFIA94H/LO7XwgcI36GA84QGXteBIwBRgB9zOxTwaZKTmZ2L03DnU8FnaUtZtYbuBf4VqzvO9EKvQQY2Ww5lzh769qcmaXTVOZPufuzQefpwGXAjWb2Lk1DWVeb2a+DjdSmEqDE3d97x/MMTQUfr64Bdrl7pbvXA88ClwacKRoVZjYcIPLv/oDztMvMbgMWAp/0+D7AZhxNf9w3Rl5vucDbZjbsfO840Qp9LZBnZmPMLIOmD5ZWBJypVWZmNI3xbnb3nwSdpyPu/nV3z3X30TT9v/7O3eNyL9Ldy4G9ZjYxsmousCnASB3ZA3zAzHpHnhdzieMPcZtZAdwW+f424PkAs7TLzBYAXwNudPfjQedpj7sXuvsQdx8deb2VAO+LPK/PS0IVeuRDj7uAfJpeEL9x91Cwqdp0GfBpmvZ0N0S+rg86VBL5EvCUmRUAs4AfBhunbZF3Es8AbwOFNL3u4upQdTN7GngTmGhmJWb2eeB+YJ6ZbadpNsb9QWZ8TxtZHwH6AS9HXmvLAg3ZTBt5O+ex4vudiYiIRCuh9tBFRKRtKnQRkSShQhcRSRIqdBGRJKFCFxFJEip0EZEkoUIXEUkS/wfJZUdiftjMZQAAAABJRU5ErkJggg==",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
@@ -1271,7 +1272,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 28,
+      "execution_count": 66,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1286,55 +1287,56 @@
           "text": [
             "Model: \"sequential\"\n",
             "_________________________________________________________________\n",
-            "Layer (type)                 Output Shape              Param #   \n",
+            " Layer (type)                Output Shape              Param #   \n",
             "=================================================================\n",
-            "dense_1 (Dense)              (None, 5)                 15        \n",
-            "_________________________________________________________________\n",
-            "dense_2 (Dense)              (None, 1)                 6         \n",
+            " dense_1 (Dense)             (None, 5)                 15        \n",
+            "                                                                 \n",
+            " dense_2 (Dense)             (None, 1)                 6         \n",
+            "                                                                 \n",
             "=================================================================\n",
             "Total params: 21\n",
             "Trainable params: 21\n",
             "Non-trainable params: 0\n",
             "_________________________________________________________________\n",
             "Epoch 1/15\n",
-            "9/9 [==============================] - 0s 26ms/step - loss: nan - accuracy: 0.4571 - val_loss: nan - val_accuracy: 0.4000\n",
+            "9/9 [==============================] - 1s 64ms/step - loss: 0.6994 - accuracy: 0.5000 - val_loss: 0.6719 - val_accuracy: 0.4667\n",
             "Epoch 2/15\n",
-            "9/9 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.5000 - val_loss: nan - val_accuracy: 0.4000\n",
+            "9/9 [==============================] - 0s 6ms/step - loss: 0.6635 - accuracy: 0.5429 - val_loss: 0.6531 - val_accuracy: 0.4667\n",
             "Epoch 3/15\n",
-            "9/9 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.5000 - val_loss: nan - val_accuracy: 0.4000\n",
+            "9/9 [==============================] - 0s 5ms/step - loss: 0.6469 - accuracy: 0.5857 - val_loss: 0.5775 - val_accuracy: 1.0000\n",
             "Epoch 4/15\n",
-            "9/9 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.5000 - val_loss: nan - val_accuracy: 0.4000\n",
+            "9/9 [==============================] - 0s 4ms/step - loss: 0.5639 - accuracy: 0.9143 - val_loss: 0.5395 - val_accuracy: 0.7333\n",
             "Epoch 5/15\n",
-            "9/9 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.5000 - val_loss: nan - val_accuracy: 0.4000\n",
+            "9/9 [==============================] - 0s 5ms/step - loss: 0.5236 - accuracy: 0.7143 - val_loss: 0.4498 - val_accuracy: 0.9333\n",
             "Epoch 6/15\n",
-            "9/9 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.5000 - val_loss: nan - val_accuracy: 0.4000\n",
+            "9/9 [==============================] - 0s 5ms/step - loss: 0.4573 - accuracy: 0.8714 - val_loss: 0.3584 - val_accuracy: 1.0000\n",
             "Epoch 7/15\n",
-            "9/9 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.5000 - val_loss: nan - val_accuracy: 0.4000\n",
+            "9/9 [==============================] - 0s 5ms/step - loss: 0.3867 - accuracy: 0.8714 - val_loss: 0.2989 - val_accuracy: 0.9333\n",
             "Epoch 8/15\n",
-            "9/9 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.5000 - val_loss: nan - val_accuracy: 0.4000\n",
+            "9/9 [==============================] - 0s 7ms/step - loss: 0.3388 - accuracy: 0.8857 - val_loss: 0.2204 - val_accuracy: 1.0000\n",
             "Epoch 9/15\n",
-            "9/9 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.5000 - val_loss: nan - val_accuracy: 0.4000\n",
+            "9/9 [==============================] - 0s 6ms/step - loss: 0.2815 - accuracy: 0.9429 - val_loss: 0.1957 - val_accuracy: 1.0000\n",
             "Epoch 10/15\n",
-            "9/9 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.5000 - val_loss: nan - val_accuracy: 0.4000\n",
+            "9/9 [==============================] - 0s 6ms/step - loss: 0.2692 - accuracy: 0.8857 - val_loss: 0.1323 - val_accuracy: 1.0000\n",
             "Epoch 11/15\n",
-            "9/9 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.5000 - val_loss: nan - val_accuracy: 0.4000\n",
+            "9/9 [==============================] - 0s 5ms/step - loss: 0.2591 - accuracy: 0.9429 - val_loss: 0.1105 - val_accuracy: 1.0000\n",
             "Epoch 12/15\n",
-            "9/9 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.5000 - val_loss: nan - val_accuracy: 0.4000\n",
+            "9/9 [==============================] - 0s 6ms/step - loss: 0.2229 - accuracy: 0.9286 - val_loss: 0.1051 - val_accuracy: 1.0000\n",
             "Epoch 13/15\n",
-            "9/9 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.5000 - val_loss: nan - val_accuracy: 0.4000\n",
+            "9/9 [==============================] - 0s 5ms/step - loss: 0.2146 - accuracy: 0.9143 - val_loss: 0.0919 - val_accuracy: 1.0000\n",
             "Epoch 14/15\n",
-            "9/9 [==============================] - 0s 4ms/step - loss: nan - accuracy: 0.5000 - val_loss: nan - val_accuracy: 0.4000\n",
+            "9/9 [==============================] - 0s 5ms/step - loss: 0.2031 - accuracy: 0.9429 - val_loss: 0.0859 - val_accuracy: 1.0000\n",
             "Epoch 15/15\n",
-            "9/9 [==============================] - 0s 3ms/step - loss: nan - accuracy: 0.5000 - val_loss: nan - val_accuracy: 0.4000\n"
+            "9/9 [==============================] - 0s 5ms/step - loss: 0.1997 - accuracy: 0.9429 - val_loss: 0.0829 - val_accuracy: 1.0000\n"
           ]
         },
         {
           "data": {
             "text/plain": [
-              "<tensorflow.python.keras.callbacks.History at 0x25c7749a0d0>"
+              "<keras.callbacks.History at 0x12894cfba30>"
             ]
           },
-          "execution_count": 28,
+          "execution_count": 66,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1403,11 +1405,6 @@
         "\n",
         "A good sample notebook from the creator of Keras on Keras and Tensorflow 2.0 can be found [here](https://t.co/k694J95PI8)."
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": []
     }
   ],
   "metadata": {
diff --git a/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb b/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb
index 5a3918b8..299757db 100644
--- a/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb
+++ b/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb
@@ -52,7 +52,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 41,
+      "execution_count": 1,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -69,7 +69,7 @@
               "'1.8.2'"
             ]
           },
-          "execution_count": 41,
+          "execution_count": 1,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -108,7 +108,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 42,
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -124,16 +124,16 @@
           "text": [
             "tensor([[1, 2],\n",
             "        [3, 4]])\n",
-            "tensor([[ 0.5323, -0.2154,  0.7426],\n",
-            "        [ 1.1795,  1.3732,  1.3171],\n",
-            "        [ 0.8288, -0.6264, -0.7545],\n",
-            "        [-1.5020,  1.7313,  0.1916],\n",
-            "        [ 1.2507,  1.5185,  0.5336],\n",
-            "        [-0.0771,  0.2244, -1.0394],\n",
-            "        [ 0.4770,  2.1839,  0.5404],\n",
-            "        [-0.4145,  1.4427, -1.6582],\n",
-            "        [-0.6721, -0.2278,  1.9838],\n",
-            "        [ 0.4876,  0.8315,  1.1669]])\n"
+            "tensor([[ 1.3577,  0.7550, -1.7503],\n",
+            "        [-0.7006,  0.1918, -0.2571],\n",
+            "        [ 0.2964, -0.3188,  0.4575],\n",
+            "        [-0.1524,  1.3446,  0.7218],\n",
+            "        [-1.4642, -1.3296, -0.5098],\n",
+            "        [ 2.2282,  0.5065,  0.6176],\n",
+            "        [-0.3013,  0.9485,  0.1195],\n",
+            "        [ 1.0261,  1.5614, -0.1013],\n",
+            "        [-0.2211, -0.4294, -2.2319],\n",
+            "        [ 1.4257, -0.6976,  0.0656]])\n"
           ]
         }
       ],
@@ -155,7 +155,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 43,
+      "execution_count": 3,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -169,16 +169,16 @@
           "output_type": "stream",
           "text": [
             "tensor([[ 0.0000,  0.0000,  0.0000],\n",
-            "        [ 0.6471,  1.5885,  0.5746],\n",
-            "        [ 0.2964, -0.4110, -1.4971],\n",
-            "        [-2.0343,  1.9467, -0.5510],\n",
-            "        [ 0.7183,  1.7339, -0.2089],\n",
-            "        [-0.6094,  0.4398, -1.7820],\n",
-            "        [-0.0553,  2.3993, -0.2021],\n",
-            "        [-0.9468,  1.6581, -2.4007],\n",
-            "        [-1.2044, -0.0124,  1.2413],\n",
-            "        [-0.0447,  1.0469,  0.4244]])\n",
-            "[1.7028859 0.8062281 2.1013143]\n"
+            "        [-2.0583, -0.5631,  1.4932],\n",
+            "        [-1.0613, -1.0738,  2.2078],\n",
+            "        [-1.5101,  0.5896,  2.4722],\n",
+            "        [-2.8219, -2.0846,  1.2405],\n",
+            "        [ 0.8706, -0.2485,  2.3679],\n",
+            "        [-1.6590,  0.1935,  1.8698],\n",
+            "        [-0.3316,  0.8065,  1.6490],\n",
+            "        [-1.5788, -1.1844, -0.4816],\n",
+            "        [ 0.0680, -1.4526,  1.8159]])\n",
+            "[3.887189   2.1276016  0.17371987]\n"
           ]
         }
       ],
@@ -200,7 +200,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 44,
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -236,7 +236,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 45,
+      "execution_count": 5,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -249,7 +249,7 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "tensor([2.0901, 8.2359, 3.0239])\n"
+            "tensor([ 3.4945,  2.5325, -2.8684])\n"
           ]
         }
       ],
@@ -272,7 +272,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 46,
+      "execution_count": 6,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -284,10 +284,10 @@
         {
           "data": {
             "text/plain": [
-              "tensor([2.0901, 8.2359, 3.0239])"
+              "tensor([ 3.4945,  2.5325, -2.8684])"
             ]
           },
-          "execution_count": 46,
+          "execution_count": 6,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -318,7 +318,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 47,
+      "execution_count": 7,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -332,8 +332,8 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "tensor([[-0.1491,  0.2151],\n",
-            "        [-0.0360,  0.0569]])\n"
+            "tensor([[-0.1728,  0.0913],\n",
+            "        [-0.1666, -0.1942]])\n"
           ]
         }
       ],
@@ -358,7 +358,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 48,
+      "execution_count": 8,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -371,10 +371,10 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "tensor([[-0.4472,  0.6452],\n",
-            "        [-0.1080,  0.1708]])\n",
-            "tensor([[-0.1491,  0.2151],\n",
-            "        [-0.0360,  0.0569]])\n"
+            "tensor([[-0.5185,  0.2739],\n",
+            "        [-0.4998, -0.5826]])\n",
+            "tensor([[-0.1728,  0.0913],\n",
+            "        [-0.1666, -0.1942]])\n"
           ]
         }
       ],
@@ -399,7 +399,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 49,
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -412,7 +412,7 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "tensor(1.2078, grad_fn=<MeanBackward0>)\n"
+            "tensor(0.9143, grad_fn=<MeanBackward0>)\n"
           ]
         }
       ],
@@ -448,7 +448,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 50,
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -461,8 +461,8 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "tensor([[-0.7453,  0.2151],\n",
-            "        [-0.0360,  0.2847]])\n"
+            "tensor([[-0.8642,  0.0913],\n",
+            "        [-0.1666, -0.9710]])\n"
           ]
         }
       ],
@@ -500,7 +500,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 51,
+      "execution_count": 11,
       "metadata": {
         "id": "nDw5mV9KEeOa"
       },
@@ -522,7 +522,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 52,
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -576,7 +576,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 53,
+      "execution_count": 13,
       "metadata": {
         "id": "j723455WwHl7",
         "trusted": true
@@ -592,7 +592,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 54,
+      "execution_count": 14,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -605,10 +605,10 @@
         {
           "data": {
             "text/plain": [
-              "<matplotlib.collections.PathCollection at 0x16318dd07f0>"
+              "<matplotlib.collections.PathCollection at 0x20b8e1f1ca0>"
             ]
           },
-          "execution_count": 54,
+          "execution_count": 14,
           "metadata": {},
           "output_type": "execute_result"
         },
@@ -650,7 +650,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 55,
+      "execution_count": 15,
       "metadata": {
         "id": "QxhI4GlB6aiH"
       },
@@ -689,7 +689,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 56,
+      "execution_count": 16,
       "metadata": {
         "id": "-991PErM7fJU"
       },
@@ -717,7 +717,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 57,
+      "execution_count": 17,
       "metadata": {
         "id": "nOuu0qpx-wAp"
       },
@@ -731,7 +731,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 58,
+      "execution_count": 18,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -774,7 +774,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 59,
+      "execution_count": 19,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -789,7 +789,7 @@
               "(tensor([1.8617], requires_grad=True), tensor([1.0711], requires_grad=True))"
             ]
           },
-          "execution_count": 59,
+          "execution_count": 19,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -800,7 +800,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 60,
+      "execution_count": 20,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -813,10 +813,10 @@
         {
           "data": {
             "text/plain": [
-              "[<matplotlib.lines.Line2D at 0x16318c5ca30>]"
+              "[<matplotlib.lines.Line2D at 0x20b8e30a850>]"
             ]
           },
-          "execution_count": 60,
+          "execution_count": 20,
           "metadata": {},
           "output_type": "execute_result"
         },
@@ -854,7 +854,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 61,
+      "execution_count": 21,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -929,7 +929,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 62,
+      "execution_count": 23,
       "metadata": {
         "id": "j0OTPkGpwHl7",
         "scrolled": false,
@@ -941,7 +941,7 @@
         "\n",
         "n = 100\n",
         "X, Y = make_classification(n_samples = n, n_features=2,\n",
-        "                           n_redundant=0, n_informative=2, flip_y=0.2,class_sep=1)\n",
+        "                           n_redundant=0, n_informative=2, flip_y=0.1,class_sep=1.5)\n",
         "X = X.astype(np.float32)\n",
         "Y = Y.astype(np.int32)\n",
         "\n",
@@ -952,7 +952,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 63,
+      "execution_count": 24,
       "metadata": {
         "id": "c-_BjSHPwHl8",
         "scrolled": false,
@@ -981,7 +981,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 64,
+      "execution_count": 25,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -997,13 +997,13 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_50828/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+            "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_89704/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
             "  fig.show()\n"
           ]
         },
         {
           "data": {
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",
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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
@@ -1035,7 +1035,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 75,
+      "execution_count": 26,
       "metadata": {
         "id": "J1KaixW-cMWJ"
       },
@@ -1079,7 +1079,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 66,
+      "execution_count": 27,
       "metadata": {
         "id": "kdDxWeCqwHl8",
         "trusted": true
@@ -1110,7 +1110,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 71,
+      "execution_count": 28,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1123,26 +1123,26 @@
         {
           "data": {
             "text/plain": [
-              "[tensor([[ 1.3383, -0.9861],\n",
-              "         [ 0.5128,  0.4330],\n",
-              "         [-0.4474, -0.2681],\n",
-              "         [-0.9866, -0.2869],\n",
-              "         [-1.0694,  0.4172],\n",
-              "         [-0.8694, -0.8607],\n",
-              "         [-0.9894,  0.3734],\n",
-              "         [-0.9951,  1.2320],\n",
-              "         [-1.3667, -1.0529],\n",
-              "         [ 0.5239, -1.9724],\n",
-              "         [-1.2474,  0.7092],\n",
-              "         [ 1.8675, -0.2057],\n",
-              "         [-1.1899,  0.4243],\n",
-              "         [ 0.7644, -0.6796],\n",
-              "         [-2.0532,  0.4223],\n",
-              "         [-0.5973, -1.2011]]),\n",
-              " tensor([1., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 0., 0., 0., 0.])]"
+              "[tensor([[ 1.5442,  2.5290],\n",
+              "         [-1.6284,  0.0772],\n",
+              "         [-1.7141,  2.4770],\n",
+              "         [-1.4951,  0.7320],\n",
+              "         [-1.6899,  0.9243],\n",
+              "         [-0.9474, -0.7681],\n",
+              "         [ 3.8597, -2.2951],\n",
+              "         [-1.3944,  1.4300],\n",
+              "         [ 4.3627,  3.1333],\n",
+              "         [-1.0973, -1.7011],\n",
+              "         [-2.5532, -0.0777],\n",
+              "         [-1.2661, -0.3167],\n",
+              "         [ 0.3921,  1.8406],\n",
+              "         [ 2.2091, -1.6045],\n",
+              "         [ 1.8383, -1.4861],\n",
+              "         [ 0.7173, -0.9718]]),\n",
+              " tensor([1., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 1., 1., 1., 1.])]"
             ]
           },
-          "execution_count": 71,
+          "execution_count": 28,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1213,7 +1213,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 79,
+      "execution_count": 29,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1226,8 +1226,8 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "tensor([[1.0522],\n",
-            "        [0.4444]], requires_grad=True) tensor([0.], requires_grad=True)\n"
+            "tensor([[ 0.1330],\n",
+            "        [-0.2810]], requires_grad=True) tensor([0.], requires_grad=True)\n"
           ]
         }
       ],
@@ -1246,7 +1246,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 80,
+      "execution_count": 30,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1261,13 +1261,13 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_50828/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
+            "C:\\Users\\dmitryso\\AppData\\Local\\Temp/ipykernel_89704/2721537645.py:17: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
             "  fig.show()\n"
           ]
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
@@ -1293,7 +1293,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 81,
+      "execution_count": 31,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1305,10 +1305,10 @@
         {
           "data": {
             "text/plain": [
-              "tensor(0.8667)"
+              "tensor(0.7333)"
             ]
           },
-          "execution_count": 81,
+          "execution_count": 31,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1357,7 +1357,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 82,
+      "execution_count": 32,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1371,8 +1371,8 @@
           "output_type": "stream",
           "text": [
             "[Parameter containing:\n",
-            "tensor([[0.6870, 0.1273]], requires_grad=True), Parameter containing:\n",
-            "tensor([0.6904], requires_grad=True)]\n"
+            "tensor([[-0.0422,  0.1821]], requires_grad=True), Parameter containing:\n",
+            "tensor([0.6582], requires_grad=True)]\n"
           ]
         }
       ],
@@ -1395,7 +1395,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 83,
+      "execution_count": 33,
       "metadata": {
         "id": "B4AxyrFMozh0"
       },
@@ -1415,7 +1415,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 84,
+      "execution_count": 34,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1428,16 +1428,16 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Epoch 0: last batch loss = 0.6965743899345398, val acc = 0.7333333492279053\n",
-            "Epoch 1: last batch loss = 0.6784186959266663, val acc = 0.7333333492279053\n",
-            "Epoch 2: last batch loss = 0.6634390354156494, val acc = 0.800000011920929\n",
-            "Epoch 3: last batch loss = 0.6509400010108948, val acc = 0.800000011920929\n",
-            "Epoch 4: last batch loss = 0.6404023170471191, val acc = 0.800000011920929\n",
-            "Epoch 5: last batch loss = 0.6314342617988586, val acc = 0.800000011920929\n",
-            "Epoch 6: last batch loss = 0.6237367391586304, val acc = 0.800000011920929\n",
-            "Epoch 7: last batch loss = 0.6170788407325745, val acc = 0.800000011920929\n",
-            "Epoch 8: last batch loss = 0.6112800240516663, val acc = 0.800000011920929\n",
-            "Epoch 9: last batch loss = 0.6061977744102478, val acc = 0.800000011920929\n"
+            "Epoch 0: last batch loss = 0.7596041560173035, val acc = 0.5333333611488342\n",
+            "Epoch 1: last batch loss = 0.6602361798286438, val acc = 0.6000000238418579\n",
+            "Epoch 2: last batch loss = 0.5847358107566833, val acc = 0.6666666865348816\n",
+            "Epoch 3: last batch loss = 0.5263020992279053, val acc = 0.7333333492279053\n",
+            "Epoch 4: last batch loss = 0.48015740513801575, val acc = 0.800000011920929\n",
+            "Epoch 5: last batch loss = 0.4430023431777954, val acc = 0.8666666746139526\n",
+            "Epoch 6: last batch loss = 0.41254672408103943, val acc = 0.8666666746139526\n",
+            "Epoch 7: last batch loss = 0.3871781527996063, val acc = 0.800000011920929\n",
+            "Epoch 8: last batch loss = 0.3657420873641968, val acc = 0.800000011920929\n",
+            "Epoch 9: last batch loss = 0.34739670157432556, val acc = 0.800000011920929\n"
           ]
         }
       ],
@@ -1469,7 +1469,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 85,
+      "execution_count": 35,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1482,16 +1482,16 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Epoch 0: last batch loss = 0.5223885774612427, val acc = 0.6000000238418579\n",
-            "Epoch 1: last batch loss = 0.5124126076698303, val acc = 0.6000000238418579\n",
-            "Epoch 2: last batch loss = 0.5077585577964783, val acc = 0.6666666865348816\n",
-            "Epoch 3: last batch loss = 0.5082311034202576, val acc = 0.800000011920929\n",
-            "Epoch 4: last batch loss = 0.5123199224472046, val acc = 0.7333333492279053\n",
-            "Epoch 5: last batch loss = 0.5183590054512024, val acc = 0.7333333492279053\n",
-            "Epoch 6: last batch loss = 0.5249963402748108, val acc = 0.7333333492279053\n",
-            "Epoch 7: last batch loss = 0.5313336253166199, val acc = 0.7333333492279053\n",
-            "Epoch 8: last batch loss = 0.5368850827217102, val acc = 0.7333333492279053\n",
-            "Epoch 9: last batch loss = 0.5414646863937378, val acc = 0.7333333492279053\n"
+            "Epoch 0: last batch loss = 0.48486900329589844, val acc = 0.7333333492279053\n",
+            "Epoch 1: last batch loss = 0.41338109970092773, val acc = 0.800000011920929\n",
+            "Epoch 2: last batch loss = 0.35756850242614746, val acc = 0.800000011920929\n",
+            "Epoch 3: last batch loss = 0.31495171785354614, val acc = 0.800000011920929\n",
+            "Epoch 4: last batch loss = 0.2824164032936096, val acc = 0.800000011920929\n",
+            "Epoch 5: last batch loss = 0.2572754919528961, val acc = 0.800000011920929\n",
+            "Epoch 6: last batch loss = 0.23751722276210785, val acc = 0.800000011920929\n",
+            "Epoch 7: last batch loss = 0.2217157930135727, val acc = 0.800000011920929\n",
+            "Epoch 8: last batch loss = 0.2088666558265686, val acc = 0.800000011920929\n",
+            "Epoch 9: last batch loss = 0.19824868440628052, val acc = 0.800000011920929\n"
           ]
         }
       ],
@@ -1526,7 +1526,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 86,
+      "execution_count": 36,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1563,7 +1563,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 87,
+      "execution_count": 37,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1576,16 +1576,16 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Epoch 0: last batch loss = 0.695976734161377, val acc = 0.6000000238418579\n",
-            "Epoch 1: last batch loss = 0.6832109093666077, val acc = 0.9333333373069763\n",
-            "Epoch 2: last batch loss = 0.6536983847618103, val acc = 0.800000011920929\n",
-            "Epoch 3: last batch loss = 0.6187950372695923, val acc = 0.7333333492279053\n",
-            "Epoch 4: last batch loss = 0.588731586933136, val acc = 0.8666666746139526\n",
-            "Epoch 5: last batch loss = 0.5675249099731445, val acc = 0.8666666746139526\n",
-            "Epoch 6: last batch loss = 0.5546941757202148, val acc = 0.8666666746139526\n",
-            "Epoch 7: last batch loss = 0.5478418469429016, val acc = 0.8666666746139526\n",
-            "Epoch 8: last batch loss = 0.5438018441200256, val acc = 0.9333333373069763\n",
-            "Epoch 9: last batch loss = 0.5394334197044373, val acc = 0.9333333373069763\n"
+            "Epoch 0: last batch loss = 0.5835739970207214, val acc = 0.800000011920929\n",
+            "Epoch 1: last batch loss = 0.4642275869846344, val acc = 0.800000011920929\n",
+            "Epoch 2: last batch loss = 0.35158076882362366, val acc = 0.800000011920929\n",
+            "Epoch 3: last batch loss = 0.26132312417030334, val acc = 0.800000011920929\n",
+            "Epoch 4: last batch loss = 0.19465585052967072, val acc = 0.800000011920929\n",
+            "Epoch 5: last batch loss = 0.14735405147075653, val acc = 0.800000011920929\n",
+            "Epoch 6: last batch loss = 0.11454981565475464, val acc = 0.800000011920929\n",
+            "Epoch 7: last batch loss = 0.09244414418935776, val acc = 0.800000011920929\n",
+            "Epoch 8: last batch loss = 0.07805468142032623, val acc = 0.800000011920929\n",
+            "Epoch 9: last batch loss = 0.06894762068986893, val acc = 0.800000011920929\n"
           ]
         }
       ],
@@ -1606,7 +1606,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 88,
+      "execution_count": 38,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1647,7 +1647,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 89,
+      "execution_count": 39,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1660,16 +1660,16 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Epoch 0: last batch loss = 0.5920814275741577, val acc = 0.8666666746139526\n",
-            "Epoch 1: last batch loss = 0.5768700242042542, val acc = 0.8666666746139526\n",
-            "Epoch 2: last batch loss = 0.5623482465744019, val acc = 0.8666666746139526\n",
-            "Epoch 3: last batch loss = 0.547980785369873, val acc = 0.8666666746139526\n",
-            "Epoch 4: last batch loss = 0.5350895524024963, val acc = 0.8666666746139526\n",
-            "Epoch 5: last batch loss = 0.5235971808433533, val acc = 0.8666666746139526\n",
-            "Epoch 6: last batch loss = 0.5140454173088074, val acc = 0.8666666746139526\n",
-            "Epoch 7: last batch loss = 0.5063431859016418, val acc = 0.9333333373069763\n",
-            "Epoch 8: last batch loss = 0.5004293322563171, val acc = 0.9333333373069763\n",
-            "Epoch 9: last batch loss = 0.4957129955291748, val acc = 0.9333333373069763\n"
+            "Epoch 0: last batch loss = 0.7821246981620789, val acc = 0.46666666865348816\n",
+            "Epoch 1: last batch loss = 0.7457502484321594, val acc = 0.5333333611488342\n",
+            "Epoch 2: last batch loss = 0.7120334506034851, val acc = 0.5333333611488342\n",
+            "Epoch 3: last batch loss = 0.6811249256134033, val acc = 0.6666666865348816\n",
+            "Epoch 4: last batch loss = 0.6533011794090271, val acc = 0.7333333492279053\n",
+            "Epoch 5: last batch loss = 0.627849280834198, val acc = 0.7333333492279053\n",
+            "Epoch 6: last batch loss = 0.6030643582344055, val acc = 0.800000011920929\n",
+            "Epoch 7: last batch loss = 0.5775002837181091, val acc = 0.800000011920929\n",
+            "Epoch 8: last batch loss = 0.5522137880325317, val acc = 0.8666666746139526\n",
+            "Epoch 9: last batch loss = 0.5250465869903564, val acc = 0.8666666746139526\n"
           ]
         }
       ],
@@ -1699,11 +1699,6 @@
         "* You can define neural network architectures using `Sequential` syntax, or inheriting a class from `torch.nn.Module`\n",
         "* For even simpler approach to defining and training a network - look into PyTorch Lightning"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": []
     }
   ],
   "metadata": {
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index ebf23aca..cd2b98bd 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -12,3 +12,8 @@ instructions provided by the bot. You will only need to do this once across all
 This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
 For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
 or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
+
+# Looking for Contributions
+
+We are currently actively looking for contributions on the following topics:
+* PyTorch Lightning (for [this section](https://github.com/microsoft/AI-For-Beginners/blob/main/3-NeuralNetworks/05-Frameworks/README.md))