original blogpost link error

Author: chiral-carbon

examples/variational_inference/bayesian_neural_network_advi.ipynb

@@ -246,9 +246,9 @@
    "source": [
     "### Variational Inference: Scaling model complexity\n",
     "\n",
-    "We could now just run a MCMC sampler like {func}`~pymc.step_methods.hmc.nuts.NUTS` which works pretty well in this case, but as I already mentioned, this will become very slow as we scale our model up to deeper architectures with more layers.\n",
+    "We could now just run a MCMC sampler like {func}`~pymc3.step_methods.hmc.nuts.NUTS` which works pretty well in this case, but as I already mentioned, this will become very slow as we scale our model up to deeper architectures with more layers.\n",
     "\n",
-    "Instead, we will use the {func}`~pymc.variational.inference.ADVI` variational inference algorithm which was added to `PyMC3`, and updated to use the operator variational inference (OPVI) framework. This is much faster and will scale better. Note, that this is a mean-field approximation so we ignore correlations in the posterior."
+    "Instead, we will use the {func}`~pymc3.variational.inference.ADVI` variational inference algorithm which was added to `PyMC3`, and updated to use the operator variational inference (OPVI) framework. This is much faster and will scale better. Note, that this is a mean-field approximation so we ignore correlations in the posterior."
    ]
   },
   {
@@ -859,7 +859,7 @@
    "source": [
     "(c) 2017 by Thomas Wiecki, updated by Maxim Kochurov\n",
     "\n",
-    "Original blog post: https://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/"
+    "Original [blog post](https://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/)."
    ]
   },
   {