First exercise

cpenalozag · Jan 15, 2018 · a9b867a · a9b867a
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diff --git a/... Networks and Deep Learning /Basics/.ipynb_checkpoints/Broadcasting demo-checkpoint.ipynb b/... Networks and Deep Learning /Basics/.ipynb_checkpoints/Broadcasting demo-checkpoint.ipynb
@@ -0,0 +1,122 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.13173302  7.80728589  6.26190867  7.2219721 ]\n",
+      " [ 4.34703125  7.83297     9.41237969  7.4847714 ]\n",
+      " [ 1.91750257  9.10735505  1.12573619  7.93226677]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "A = 10*np.random.rand(3,4)\n",
+    "print(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 18.75768804  13.9073695   21.41652281  15.24013189]\n"
+     ]
+    }
+   ],
+   "source": [
+    "cal = A.sum(axis=0)\n",
+    "print(cal)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 29.81471408  50.34378724  36.86021364  64.43175012]\n",
+      " [ 26.96686398  38.48118926  33.10381054  11.96405678]\n",
+      " [ 43.21842194  11.1750235   30.03597582  23.6041931 ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "percentage = 100*A/(cal.reshape(1,4))\n",
+    "print(percentage)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.random.randn(3, 3)\n",
+    "b = np.random.randn(3, 1)\n",
+    "c = a*b\n",
+    "print (c.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "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": "3.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/...tworks and Deep Learning /Basics/.ipynb_checkpoints/Python-Numpy Vectors-checkpoint.ipynb b/...tworks and Deep Learning /Basics/.ipynb_checkpoints/Python-Numpy Vectors-checkpoint.ipynb
@@ -0,0 +1,151 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.60389225]\n",
+      " [ 0.6429397 ]\n",
+      " [ 0.35553732]\n",
+      " [ 0.11588369]\n",
+      " [ 0.44658051]]\n",
+      "(5, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "a = np.random.rand(5,1)\n",
+    "print (a)\n",
+    "print (a.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.60389225  0.6429397   0.35553732  0.11588369  0.44658051]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (a.T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.36468585  0.3882663   0.21470623  0.06998126  0.26968651]\n",
+      " [ 0.3882663   0.41337146  0.22858906  0.07450623  0.28712434]\n",
+      " [ 0.21470623  0.22858906  0.12640679  0.04120098  0.15877604]\n",
+      " [ 0.06998126  0.07450623  0.04120098  0.01342903  0.0517514 ]\n",
+      " [ 0.26968651  0.28712434  0.15877604  0.0517514   0.19943415]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.dot(a,a.T))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "def basic_sigmoid(x):\n",
+    "    \"\"\"\n",
+    "    Compute sigmoid of x.\n",
+    "\n",
+    "    Arguments:\n",
+    "    x -- A scalar\n",
+    "\n",
+    "    Return:\n",
+    "    s -- sigmoid(x)\n",
+    "    \"\"\"\n",
+    "    s = 1/(1+math.exp(-x))\n",
+    "\n",
+    "    \n",
+    "    return s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9933071490757153"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "basic_sigmoid(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "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": "3.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/1. Neural Networks and Deep Learning /Basics/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/1. Neural Networks and Deep Learning /Basics/.ipynb_checkpoints/Untitled-checkpoint.ipynb
diff --git a/1. Neural Networks and Deep Learning /Basics/Broadcasting demo.ipynb b/1. Neural Networks and Deep Learning /Basics/Broadcasting demo.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -11,9 +11,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 5.59255106  7.00149651  7.89417606  9.8194837 ]\n",
-      " [ 5.05836022  5.35172118  7.08968514  1.82333803]\n",
-      " [ 8.10677676  1.55415181  6.43266161  3.59731016]]\n"
+      "[[ 0.13173302  7.80728589  6.26190867  7.2219721 ]\n",
+      " [ 4.34703125  7.83297     9.41237969  7.4847714 ]\n",
+      " [ 1.91750257  9.10735505  1.12573619  7.93226677]]\n"
      ]
     }
    ],
@@ -66,6 +66,28 @@
     "print(percentage)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.random.randn(3, 3)\n",
+    "b = np.random.randn(3, 1)\n",
+    "c = a*b\n",
+    "print (c.shape)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,