Changed the tutorial 0 to mark the difference between versions of TF 1.4 and 1.3

Author: crscardellino

tensorflow_tutorial_0.ipynb

@@ -10,21 +10,21 @@
     "\n",
     "## Environment configuration\n",
     "\n",
-    "There are two major package managers in Python: `pip` and `conda`. For this tutorial we will be using conda which, besides being a package manager is also useful as a version manager. There are two main ways to install conda: [Anaconda](https://conda.io/docs/install/quick.html) and [Miniconda](https://conda.io/miniconda.html).\n",
+    "There are two major package managers in Python: `pip` and `conda`. For this tutorial we will be using conda which, besides being a package manager is also useful as a version manager. There are two main ways to install conda: [Anaconda](https://conda.io/docs/install/quick.html) and [Miniconda](https://conda.io/miniconda.html). For this tutorial we will be using Python version 3.6.\n",
     "\n",
     "In order to install tensorflow we recommend following the [official documentation](https://www.tensorflow.org/install/install_linux#installing_with_anaconda). In particular, for the conda installation, they advise to use pip instead of conda as the only available Anaconda package for tensorflow is not actively mantained.\n",
     "\n",
-    "All the available tensorflow versions (for both Python 2 and 3 and with CPU and GPU support) can be found [in this link](https://www.tensorflow.org/install/install_linux#the_url_of_the_tensorflow_python_package). For this course we will be using: https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.3.0-cp35-cp35m-linux_x86_64.whl\n",
+    "All the available tensorflow versions (for both Python 2 and 3 and with CPU and GPU support) can be found [in this link](https://www.tensorflow.org/install/install_linux#the_url_of_the_tensorflow_python_package). For this course we will be using tensorflow 1.4<sup>1</sup>: `https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.4.0-cp36-cp36m-linux_x86_64.whl`\n",
     "\n",
-    "The commands to setup the environment are the following\n",
+    "The commands to setup the environment are the following:\n",
     "\n",
     "```\n",
     "$ wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
     "$ bash Miniconda3-latest-Linux-x86_64.sh\n",
-    "$ conda create --name pydata python=3.5\n",
+    "$ conda create --name pydata python=3.6\n",
     "$ source activate pydata\n",
     "(pydata) $ conda install numpy scipy jupyter nb_conda\n",
-    "(pydata) $ export tfBinaryURL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.4.0-cp35-cp35m-linux_x86_64.whl\n",
+    "(pydata) $ export tfBinaryURL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.4.0-cp36-cp36m-linux_x86_64.whl\n",
     "(pydata) $ pip install $tfBinaryURL\n",
     "(pydata) $ jupyter notebook\n",
     "```\n",
@@ -36,17 +36,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.4.0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import tensorflow as tf\n",
     "print(tf.__version__)"
@@ -63,7 +55,6 @@
     "```\n",
     "(pydata) $ conda install gensim\n",
     "(pydata) $ conda install scikit-learn\n",
-    "(pydata) $ conda install seaborn\n",
     "```"
    ]
   },
@@ -94,14 +85,23 @@
     "\n",
     "The ouput should be an executable file inside your anaconda environment. Something like `~/miniconda/envs/pydata/bin/tensorboard`"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "<sup>1</sup> The tensorflow version used in this tutorial is version 1.4 because there are some functionalities needed for tensorboard to follow the model evaluation that are available for this version and not version 1.3. However, most of the tutorial can be done with version 1.3 of tensorflow (in case the environment was already set up)."
+   ]
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda env:keras]",
+   "display_name": "Python [conda env:pydata]",
    "language": "python",
-   "name": "conda-env-keras-py"
+   "name": "conda-env-pydata-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -113,7 +113,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.4"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

tensorflow_tutorial_1.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Train and visualize a model in Tensorflow - Part 1 (optional): Dataset Preprocessing\n",
     "\n",
-    "This notebook is optional for those who want to follow how we got the dataset we are using for the tutorial. For this tutorial you need to previously do all the configuration steps explained in **tutorial 0**, plus install the optional libraries (particularly `scikit-learn` and `gensim`).\n",
+    "This notebook is optional for those who want to follow how we got the dataset we are using for the tutorial. For this tutorial you need to previously do all the configuration steps explained in **tutorial 0**, plus install the optional libraries.\n",
     "\n",
     "## Dataset Download\n",
     "\n",
@@ -226,9 +226,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda env:keras]",
+   "display_name": "Python [conda env:pydata]",
    "language": "python",
-   "name": "conda-env-keras-py"
+   "name": "conda-env-pydata-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -240,7 +240,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,