Added Linear Regression Model and MNIST.

Author: sherrym

0_tf_hello_world.ipynb

@@ -4,14 +4,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can press `shift + enter` to quickly advance through each line of a notebook.  Try it now!"
+    "You can use `ctrl-enter` to execute each snippet of code.  The first snippet will check that TensorFlow is installed. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This snippet of Python creates an extremely simple graph and runs."
+    "Now, create an extremely simple graph and run it!"
    ]
   },
   {
@@ -22,18 +33,10 @@
    },
    "outputs": [],
    "source": [
-    "import tensorflow as tf\n",
-    "\n",
     "hello = tf.constant('Hello, TensorFlow!')\n",
     "sess = tf.Session()\n",
-    "print(sess.run(hello))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you see \"Hello, TensorFlow!\", everything is working well."
+    "print(sess.run(hello)) \n",
+    "print tf.__version__"
    ]
   }
  ],

Linear_Regression_Model.ipynb

@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3\n",
+    "x_data = np.random.rand(100).astype(np.float32)\n",
+    "y_data = x_data * 0.1 + 0.3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Try to find values for W and b that compute y_data = W * x_data + b\n",
+    "# (We know that W should be 0.1 and b 0.3, but TensorFlow will\n",
+    "# figure that out for us.)\n",
+    "W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))\n",
+    "b = tf.Variable(tf.zeros([1]))\n",
+    "y = W * x_data + b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Minimize the mean squared errors.\n",
+    "loss = tf.reduce_mean(tf.square(y - y_data))\n",
+    "optimizer = tf.train.GradientDescentOptimizer(0.5)\n",
+    "train = optimizer.minimize(loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Before starting, initialize the variables.  We will 'run' this first.\n",
+    "init = tf.initialize_all_variables()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Launch the graph.\n",
+    "sess = tf.Session()\n",
+    "sess.run(init)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Fit the line.\n",
+    "for step in range(201):\n",
+    "    sess.run(train)\n",
+    "    if step % 20 == 0:\n",
+    "        print(step, sess.run(W), sess.run(b))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}