Minor typo fix (#2604)

Author: HassanNaseri

docs/source/notebooks/bayesian_neural_network_advi.ipynb

@@ -35,7 +35,7 @@
     "* Architectural: A lot of innovation comes from changing the input layers, like for convolutional neural nets, or the output layers, like for [MDNs](http://cbonnett.github.io/MDN_EDWARD_KERAS_TF.html).\n",
     "\n",
     "### Bridging Deep Learning and Probabilistic Programming\n",
-    "On one hand we Probabilistic Programming which allows us to build rather small and focused models in a very principled and well-understood way to gain insight into our data; on the other hand we have deep learning which uses many heuristics to train huge and highly complex models that are amazing at prediction. Recent innovations in variational inference allow probabilistic programming to scale model complexity as well as data size. We are thus at the cusp of being able to combine these two approaches to hopefully unlock new innovations in Machine Learning. For more motivation, see also [Dustin Tran's](https://twitter.com/dustinvtran) recent [blog post](http://dustintran.com/blog/a-quick-update-edward-and-some-motivations/).\n",
+    "On one hand we have Probabilistic Programming which allows us to build rather small and focused models in a very principled and well-understood way to gain insight into our data; on the other hand we have deep learning which uses many heuristics to train huge and highly complex models that are amazing at prediction. Recent innovations in variational inference allow probabilistic programming to scale model complexity as well as data size. We are thus at the cusp of being able to combine these two approaches to hopefully unlock new innovations in Machine Learning. For more motivation, see also [Dustin Tran's](https://twitter.com/dustinvtran) recent [blog post](http://dustintran.com/blog/a-quick-update-edward-and-some-motivations/).\n",
     "\n",
     "While this would allow Probabilistic Programming to be applied to a much wider set of interesting problems, I believe this bridging also holds great promise for innovations in Deep Learning. Some ideas are:\n",
     "* **Uncertainty in predictions**: As we will see below, the Bayesian Neural Network informs us about the uncertainty in its predictions. I think uncertainty is an underappreciated concept in Machine Learning as it's clearly important for real-world applications. But it could also be useful in training. For example, we could train the model specifically on samples it is most uncertain about.\n",