Add digit recognition demo.

Author: trekhleb

README.md

@@ -36,8 +36,8 @@ _Usage examples: spam-filters, language detection, finding similar documents, ha
 
 - 📗 [Math | Logistic Regression](homemade/logistic_regression) - theory and links for further readings
 - ⚙️ [Code | Logistic Regression](homemade/logistic_regression/logistic_regression.py) - implementation example
-- ▶️ [Demo | Linear Logistic Regression](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/logistic_regression/linear_logistic_regression_demo.ipynb) - predict Iris flower `class` based on `petal_length` and `petal_width`
-- ▶️ [Demo | Non-linear Logistic Regression](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/logistic_regression/non_linear_logistic_regression_demo.ipynb) - predict microchip `validity` based on `param_1` and `param_2`
+- ▶️ [Demo | Logistic Regression With Linear Boundary](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/logistic_regression/logistic_regression_with_linear_boundary_demo.ipynb) - predict Iris flower `class` based on `petal_length` and `petal_width`
+- ▶️ [Demo | Logistic Regression With Non-Linear Boundary](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/logistic_regression/logistic_regression_with_non_linear_boundary_demo.ipynb) - predict microchip `validity` based on `param_1` and `param_2`
 
 ## Unsupervised Learning
 

data/mnist-demo.csv

@@ -2,11 +2,9 @@
 
 ## Jupyter Demos
 
-▶️ [Demo | Univariate Linear Regression](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/linear_regression/univariate_linear_regression_demo.ipynb) - predict `country happiness` score by `economy GDP`
+▶️ [Demo | Logistic Regression With Linear Boundary](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/logistic_regression/logistic_regression_with_linear_boundary_demo.ipynb) - predict Iris flower `class` based on `petal_length` and `petal_width`
 
-▶️ [Demo | Multivariate Linear Regression](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/linear_regression/multivariate_linear_regression_demo.ipynb) - predict `country happiness` score by `economy GDP` and `freedom index`
-
-▶️ [Demo | Non-linear Regression](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/linear_regression/non_linear_regression_demo.ipynb) - use linear regression with _polynomial_ and _sinusoid_ features to predict non-linear dependencies.
+▶️ [Demo | Logistic Regression With Non-Linear Boundary](https://nbviewer.jupyter.org/github/trekhleb/homemade-machine-learning/blob/master/notebooks/logistic_regression/logistic_regression_with_non_linear_boundary_demo.ipynb) - predict microchip `validity` based on `param_1` and `param_2`
 
 ## Definition
 

homemade/logistic_regression/README.md

@@ -12,7 +12,7 @@ def normalize(features):
     """
 
     # Copy original array to prevent it from changes.
-    features_normalized = np.copy(features)
+    features_normalized = np.copy(features).astype(float)
 
     # Get average values for each feature (column) in X.
     features_mean = np.mean(features, 0)

homemade/utils/features/normalize.py

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Linear Logistic Regression Demo\n",
+    "# Logistic Regression With Linear Boundary Demo\n",
     "\n",
     "> ☝Before moving on with this demo you might want to take a look at:\n",
     "> - 📗[Math behind the Logistic Regression](https://github.com/trekhleb/homemade-machine-learning/tree/master/homemade/logistic_regression)\n",

notebooks/logistic_regression/linear_logistic_regression_demo.ipynb renamed to notebooks/logistic_regression/logistic_regression_with_linear_boundary_demo.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Non-Linear Logistic Regression Demo\n",
+    "# Logistic Regression With Non-Linear Boundary Demo\n",
     "\n",
     "> ☝Before moving on with this demo you might want to take a look at:\n",
     "> - 📗[Math behind the Logistic Regression](https://github.com/trekhleb/homemade-machine-learning/tree/master/homemade/logistic_regression)\n",