Adding TensorFlow install instructions.

notebooks/08_Introduction_to_TensorFlow.ipynb

@@ -47,7 +47,23 @@
     "\n",
     "TensorFlow allows complex computation graphs (also known as data flow graphs in TensorFlow parlance) to be defined via a Python interface, with efficient C++ implementations for running the corresponding operations on different devices. TensorFlow also includes tools for automatic gradient computation and a large and growing suite of pre-define operations useful for gradient-based training of machine learning models.\n",
     "\n",
-    "In this notebook we will introduce some of the basic elements of constructing, training and evaluating models with TensorFlow. This will use similar material to some of the [official TensorFlow tutorials](https://www.tensorflow.org/tutorials/) but with an additional emphasis of making links to the material covered in this course last semester. Those already familiar with TensorFlow may wish to skip these introductory exercises and start on one of the introduction notebooks for the projects this semester."
+    "In this notebook we will introduce some of the basic elements of constructing, training and evaluating models with TensorFlow. This will use similar material to some of the [official TensorFlow tutorials](https://www.tensorflow.org/tutorials/) but with an additional emphasis of making links to the material covered in this course last semester. For those who have not used a computational graph framework such as TensorFlow or Theano before you may find the [basic usage tutorial](https://www.tensorflow.org/get_started/basic_usage) useful to go through\n",
+    "\n",
+    "### Installing TensorFlow\n",
+    "\n",
+    "To install TensorFlow, open a terminal, activate your Conda `mlp` environment using\n",
+    "\n",
+    "```\n",
+    "source activate mlp\n",
+    "```\n",
+    "\n",
+    "and then run\n",
+    "\n",
+    "```\n",
+    "conda install -c conda-forge tensorflow=0.12.1\n",
+    "```\n",
+    "\n",
+    "This should locally install version 0.12.1 (the latest stable version as of the time of writing) of TensorFlow in to your Conda environment. After installing TensorFlow you may need to restart the kernel in the notebook to allow it to imported."
    ]
   },
   {
@@ -84,7 +100,7 @@
     "  * `shape` (optional) : An iterable defining the shape (size of each dimension) of the tensor e.g. `shape=(5, 2)` would indicate a 2D tensor ($\\sim$ matrix) with first dimension of size 5 and second of size 2. An entry of `None` in the shape definition corresponds to the corresponding dimension size being left unspecified, so for example `shape=(None, 28, 28)` would allow any 3D inputs with final two dimensions of size 28 to be inputted.\n",
     "  * `name` (optional): String argument defining a name for the tensor which can be useful when visualising a computation graph and for debugging purposes.\n",
     "  \n",
-    "As we will generally be working with batches of datapoints, both the `inputs` and `targets` will be 2D tensors with first dimension corresponding to the batch size and second dimension the size of each input or output vector. As in the previous semester's work we will use a 1-of-$k$ encoding for the class targets so each output corresponds to a vector of length 10 (number of digit classes)."
+    "As we will generally be working with batches of datapoints, both the `inputs` and `targets` will be 2D tensors with first dimension corresponding to the batch size (set as `None` here to allow it tand second dimension the size of each input or output vector. As in the previous semester's work we will use a 1-of-$k$ encoding for the class targets so each output corresponds to a vector of length 10 (number of digit classes)."
    ]
   },
   {