revert to unaliased numpy

Author: labarba

notebooks_en/2_Logistic_Regression.ipynb

@@ -693,9 +693,7 @@
     "The main function you will need is `autograd.grad()`, which takes a scalar-valued Python function as argument, and returns another function that evaluates to its derivative. \n",
     "It's what we use in the optimization loop to perform gradient descent.\n",
     "\n",
-    "In addition, `autograd.numpy` is a wrapper to the NumPy library. This allows you to call your favorite NumPy methods with `autograd` keeping track of every operation so it can give you the derivative (via the chain rule).\n",
-    "We ill import it using the alias (`as np`), consistent with the tutorials and documentation that you will find online.\n",
-    "Up to now in the _Engineering Computations_ series of modules, we had refrained from using the aliased form of the import statements, just to have more explicit and readable code. "
+    "In addition, `autograd.numpy` is a wrapper to the NumPy library. This allows you to call your favorite NumPy methods with `autograd` keeping track of every operation so it can give you the derivative (via the chain rule)."
    ]
   },
   {
@@ -705,7 +703,7 @@
    "outputs": [],
    "source": [
     "# import the autograd-wrapped version of numpy\n",
-    "import autograd.numpy as np"
+    "from autograd import numpy"
    ]
   },
   {
@@ -732,11 +730,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# note: the namespace np is the autograd wrapper to NumPy\n",
+    "# note: the namespace numpy is the autograd wrapper to NumPy\n",
     "\n",
     "def logistic(z):\n",
     "    '''The logistic function'''\n",
-    "    return 1 / (1 + np.exp(-z))\n",
+    "    return 1 / (1 + numpy.exp(-z))\n",
     "    \n",
     "def logistic_model(params, x):\n",
     "    '''A prediction model based on the logistic function composed with wx+b\n",
@@ -756,7 +754,7 @@
     "       model:  the Python function for the logistic model\n",
     "       x, y:   arrays of input data to the model'''\n",
     "    y_pred = model(params, x)\n",
-    "    return -np.mean(y * np.log(y_pred) + (1-y) * np.log(1 - y_pred))"
+    "    return -numpy.mean(y * numpy.log(y_pred) + (1-y) * numpy.log(1 - y_pred))"
    ]
   },
   {
@@ -816,7 +814,7 @@
    ],
    "source": [
     "numpy.random.seed(0)\n",
-    "params = np.random.rand(2)\n",
+    "params = numpy.random.rand(2)\n",
     "print(params)"
    ]
   },
@@ -891,9 +889,9 @@
    "source": [
     "max_iter = 5000\n",
     "i = 0\n",
-    "descent = np.ones(len(params))\n",
+    "descent = numpy.ones(len(params))\n",
     "\n",
-    "while np.linalg.norm(descent) > 0.001 and i < max_iter:\n",
+    "while numpy.linalg.norm(descent) > 0.001 and i < max_iter:\n",
     "\n",
     "    descent = gradient(params, logistic_model, x_data, y_data)\n",
     "    params = params - descent * 0.01\n",