Fix some references and minor changes

Author: ricardoV94

docs/source/learn/core_notebooks/pymc_aesara.ipynb

@@ -1003,9 +1003,7 @@
     }
    ],
    "source": [
-    "rng = np.random.default_rng()\n",
-    "\n",
-    "a = rng.normal(loc=0, scale=1, size=1_000)\n",
+    "a = np.random.normal(loc=0, scale=1, size=1_000)\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(8, 6))\n",
     "ax.hist(a, color=\"C0\", bins=15)\n",
@@ -1025,7 +1023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 57,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -1035,17 +1033,17 @@
     {
      "data": {
       "text/plain": [
-       "aesara.tensor.var.TensorVariable"
+       "TensorType(float64, ())"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "y = at.random.normal(loc=0, scale=1, name=\"y\")\n",
-    "type(y)"
+    "y.type"
    ]
   },
   {
@@ -1436,7 +1434,7 @@
     }
    },
    "source": [
-    "We are just creating random variables like we saw before, but now registering them in a PyMC model. To extract the list of random variables we can simply do:"
+    "We are just creating random variables like we saw before, but now registering them in a `pymc` model. To extract the list of random variables we can simply do:"
    ]
   },
   {
@@ -1673,7 +1671,7 @@
     }
    },
    "source": [
-    "PyMC is able to convert `RandomVariable`s to their respective probability functions. One simple way is to use {func}`~pm.distributions.logprob.logp`, which takes as first input a RandomVariable, and as second input the value at which the logp is evaluated (we will discuss this in more detail later)."
+    "`pymc` is able to convert `RandomVariable`s to their respective probability functions. One simple way is to use {func}`~pymc.distributions.logprob.logp`, which takes as first input a RandomVariable, and as second input the value at which the logp is evaluated (we will discuss this in more detail later)."
    ]
   },
   {
@@ -1765,7 +1763,7 @@
    },
    "source": [
     ":::{tip}\n",
-    "There is also a handy PyMC function to compute the log cumulative probability of a random variable {func}`~pm.distributions.logprob.logcdf`."
+    "There is also a handy `pymc` function to compute the log cumulative probability of a random variable {func}`~pymc.distributions.logprob.logcdf`."
    ]
   },
   {
@@ -1846,7 +1844,7 @@
     }
    },
    "source": [
-    "PyMC Models provide some helpful routines to facilitating the conversion of `RandomVariable`s to probability functions. {meth}`~pymc.Model.logpt`, for instance can be used to extract the joint probability of all variables in the model:"
+    "`pymc` models provide some helpful routines to facilitating the conversion of `RandomVariable`s to probability functions. {meth}`~pymc.Model.logpt`, for instance can be used to extract the joint probability of all variables in the model:"
    ]
   },
   {