Modified dependency string for each of the tutorials

Author: Carlos Araya

tutorials/CIFAR_TorchVision_Captum_Insights.ipynb

@@ -15,9 +15,7 @@
     "\n",
     "More details about the model can be found here: https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py\n  ",
     "\n  ",
-    "**Note:**  This tutorial uses torchvision.  To download torchvision you can do one of the following:\n  ",
-    "- **Conda:** conda install torchvision -c pytorch\n  ",
-    "- **Pip:** pip3 install torchvision"
+    "**Note:** Before running this tutorial, please install the torchvision, and IPython packages."
    ]
   },
   {

tutorials/CIFAR_TorchVision_Interpret.ipynb

@@ -15,9 +15,7 @@
     "\n",
     "More details about the model can be found here: https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py\n  ",
     "\n  ",
-    "**Note:**  This tutorial uses torchvision.  To download torchvision you can do one of the following:\n  ",
-    "- **Conda:** conda install torchvision -c pytorch\n  ",
-    "- **Pip:** pip3 install torchvision"
+    "**Note:** Before running this tutorial, please install the torchvision, and matplotlib packages."
    ]
   },
   {

tutorials/IMDB_TorchText_Interpret.ipynb

@@ -15,7 +15,9 @@
    "source": [
     "This notebook loads pretrained CNN model for sentiment analysis on IMDB dataset. It makes predictions on test samples and interprets those predictions using integrated gradients method.\n",
     "\n",
-    "The model was trained using an open source sentiment analysis tutorials described in: https://github.com/bentrevett/pytorch-sentiment-analysis/blob/master/4%20-%20Convolutional%20Sentiment%20Analysis.ipynb"
+    "The model was trained using an open source sentiment analysis tutorials described in: https://github.com/bentrevett/pytorch-sentiment-analysis/blob/master/4%20-%20Convolutional%20Sentiment%20Analysis.ipynb\n  ",
+    "\n  ",
+    "**Note:** Before running this tutorial, please install the spacy package, and its NLP modules for English language."
    ]
   },
   {

tutorials/Multimodal_VQA_Interpret.ipynb

@@ -16,9 +16,7 @@
     "As a reference VQA model we use the following open source implementation:\n",
     "https://github.com/Cyanogenoid/pytorch-vqa\n  ",
     "\n  ",
-    "**Note:**  This tutorial uses torchvision.  To download torchvision you can do one of the following:\n  ",
-    "- **Conda:** conda install torchvision -c pytorch\n  ",
-    "- **Pip:** pip3 install torchvision"
+    "**Note:** Before running this tutorial, please install the torchvision, PIL, and matplotlib packages."
    ]
   },
   {

tutorials/Resnet_TorchVision_Interpret.ipynb

@@ -15,9 +15,7 @@
     "\n",
     "The interpretation algorithms that we use in this notebook are Integrated Gradients (w/o noise tunnel) and GradientShap. Noise tunnel allows to smoothen the attributions after adding gaussian noise to each input sample.\n  ",
     "\n  ",
-    "**Note:**  This tutorial uses torchvision.  To download torchvision you can do one of the following:\n  ",
-    "- **Conda:** conda install torchvision -c pytorch\n  ",
-    "- **Pip:** pip3 install torchvision"
+    "**Note:** Before running this tutorial, please install the torchvision, PIL, and matplotlib packages."
    ]
   },
   {

tutorials/Titanic_Basic_Interpret.ipynb

@@ -11,7 +11,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this notebook, we will demonstrate the basic features of the Captum interpretability library through an example model trained on the Titanic survival data. We will first train a deep neural network on the data using PyTorch and use Captum to understand which of the features were most important and how the network reached its prediction."
+    "In this notebook, we will demonstrate the basic features of the Captum interpretability library through an example model trained on the Titanic survival data. We will first train a deep neural network on the data using PyTorch and use Captum to understand which of the features were most important and how the network reached its prediction.\n  ",
+    "\n  ",
+    "**Note:** Before running this tutorial, please install the scipy, pandas, and matplotlib packages."
    ]
   },
   {