apply analysis tutorial fixes

tutorials/03_density_estimators.ipynb

@@ -25,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One option is to use one of set of preconfigured density estimators by passing a string in the `density_estimator` keyword argument to the inference object (`NPE` or `NLE`), e.g., \"maf\" to use a Masked Autoregressive Flow, of \"nsf\" to use a Neural Spline Flow with default hyperparameters.\n"
+    "One option is to use one of the preconfigured density estimators by passing a string in the `density_estimator` keyword argument to the inference object (`NPE` or `NLE`), e.g., \"maf\" to use a Masked Autoregressive Flow, of \"nsf\" to use a Neural Spline Flow with default hyperparameters.\n"
    ]
   },
   {
@@ -101,7 +101,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It is also possible to pass an `embedding_net` to `posterior_nn()` which learn summary\n",
+    "It is also possible to pass an `embedding_net` to `posterior_nn()` which learns summary\n",
     "statistics from high-dimensional simulation outputs. You can find a more detailed\n",
     "tutorial on this in [04_embedding_networks](04_embedding_networks.md).\n"
    ]
@@ -129,13 +129,6 @@
     "\n",
     "See more information on the [Reference API page](https://sbi-dev.github.io/sbi/reference/models/#sbi.neural_nets.density_estimators.ConditionalDensityEstimator)."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {