Clean up notebooks

3-NeuralNetworks/03-Perceptron/lab/PerceptronMultiClass.ipynb

@@ -49,7 +49,7 @@
    "source": [
     "def train(positive_examples, negative_examples, num_iterations = 100):\n",
     "    num_dims = positive_examples.shape[1]\n",
-    "    weights = np.zeros((num_dims,1)) # инициализируем веса\n",
+    "    weights = np.zeros((num_dims,1)) # initialize weights\n",
     "    \n",
     "    pos_count = positive_examples.shape[0]\n",
     "    neg_count = negative_examples.shape[0]\n",

3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb

@@ -68,7 +68,7 @@
     "X = X.astype(np.float32)\n",
     "Y = Y.astype(np.int32)\n",
     "\n",
-    "# Разбиваем на обучающую и тестовые выборки\n",
+    "# Split into train and test dataset\n",
     "train_x, test_x = np.split(X, [n*8//10])\n",
     "train_labels, test_labels = np.split(Y, [n*8//10])"
    ]
@@ -708,7 +708,7 @@
    "source": [
     "To compute $\\partial\\mathcal{L}/\\partial W$ we can use the **chaining rule** for computing derivatives of a composite function, as you can see in the formulae above. It corresponds to the following idea:\n",
     "\n",
-    "* Suppose under given input we have obtained loss $\\Delta\\mathcal{L}$\n",
+    "* Suppose under given input we have obtanes loss $\\Delta\\mathcal{L}$\n",
     "* To minimize it, we would have to adjust softmax output $p$ by value $\\Delta p = (\\partial\\mathcal{L}/\\partial p)\\Delta\\mathcal{L}$  \n",
     "* This corresponds to the changes to node $z$ by $\\Delta z = (\\partial\\mathcal{p}/\\partial z)\\Delta p$\n",
     "* To minimize this error, we need to adjust parameters accordingly: $\\Delta W = (\\partial\\mathcal{z}/\\partial W)\\Delta z$ (and the same for $b$)\n",
@@ -821,7 +821,7 @@
    "source": [
     "## Training the Model\n",
     "\n",
-    "Now we are ready to write the **training loop**, which will go through our dataset, and perform the optimization minibatch by minibatch. One complete pass through the dataset is often called **an epoch**:"
+    "Now we are ready to write the **training loop**, which will go through our dataset, and perform the optimization minibatch by minibatch.One complete pass through the dataset is often called **an epoch**:"
    ]
   },
   {
@@ -1169,7 +1169,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " Adding several layers make sense, because unlike one-layer network, multi-layered model will be able to accurately classify sets that are not linearly separable. I.e., a model with several layers will be **reacher**.\n",
+    " Adding several layers make sense, because unlike one-layer network, multi-layered model will be able to accuratley classify sets that are not linearly separable. I.e., a model with several layers will be **reacher**.\n",
     "\n",
     "> It can be demonstrated that with sufficient number of neurons a two-layered model is capable to classifying any convex set of data points, and three-layered network can classify virtually any set.\n",
     "\n",