Update instructions for sentiment-analysis

Fix README and notebook

Author: Paolo Di Francesco

Introduction/README.md

@@ -77,7 +77,7 @@ When you're finished, return here to move on to the next module.
 
 ## Natural Language Processing Use Case:  Sentiment Analysis  
 
-In contrast to the previous modules, which used some of Amazon SageMaker's built-in algorithms, in this module we'll use a deep learning framework within Amazon SageMaker with our own script defining a custom model.  Assuming you have cloned this repository into your notebook environment (which you should do if you haven't), open the `notebooks` directory of the repository and click on the `sentiment-analysis.ipynb` notebook to open it.  Make sure you are using the `Python 3 (Data Science)` kernel if you're using SageMaker Studio.
+In contrast to the previous modules, which used some of Amazon SageMaker's built-in algorithms, in this module we'll use a deep learning framework within Amazon SageMaker with our own script defining a custom model.  Assuming you have cloned this repository into your notebook environment (which you should do if you haven't), open the `notebooks` directory of the repository and click on the `sentiment-analysis.ipynb` notebook to open it.  Make sure you are using the `Python 3 (TensorFlow 2.3 Python 3.7 CPU Optimized)` kernel if you're using SageMaker Studio.
 
 When you're finished, return here and go on to the Extra Credit module or Cleanup Guide.  
 

notebooks/sentiment-analysis.ipynb

@@ -16,6 +16,9 @@
     "- Batch Transform: for offline, asynchronous predictions on large batches of data. \n",
     "- Instance type choices:  many different kinds of CPU and GPU instances are available in SageMaker, and are applicable to different use cases.\n",
     "\n",
+    "***Prerequisites:***\n",
+    "- In SageMaker Studio, for kernel select **Python 3 (TensorFlow 2.3 Python 3.7 CPU Optimized)**; for a SageMaker Notebook Instance, select the kernel **conda_tensorflow2_py36**.\n",
+    "\n",
     "#  Prepare the dataset\n",
     "\n",
     "We'll begin by loading the reviews dataset, and padding the reviews so all reviews have the same length.  Each review is represented as an array of numbers, where each number represents an indexed word.  We'll also pad shorter reviews to match a maximum specified length."
@@ -27,6 +30,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "!pip install matplotlib==3.4.1\n",
     "%matplotlib inline\n",
     "\n",
     "import logging\n",