Update MNIST_Landmarks.ipynb per AMS suggestions

notebooks/MNIST_Landmarks.ipynb

@@ -10,16 +10,6 @@
     "UMAP is useful for generating visualisations, but if you want to make use of UMAP more generally for machine learning tasks it is important to be be able to train a model and then later pass new data to the model and have it transform that data into the learned space. For example if we use UMAP to learn a latent space and then train a classifier on data transformed into the latent space then the classifier is only useful for prediction if we can transform data for which we want a prediction into the latent space the classifier uses."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "464ec109-f662-4201-8c77-ea7f87beb5a3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install keras scikit-learn umap-learn matplotlib numpy pandas"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -50,7 +40,6 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "import numpy as np\n",
-    "import pandas as pd"
    ]
   },
   {