Fix to MNIST classifier tutorial (#154)

* fixes #112"

* update nb

CHANGELOG.md

@@ -5,6 +5,13 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+
+## [0.28.2] - 2022-03-03
+
+### Added
+
+- A brief overview of the tutorial structure in the MNIST classification tutorial.
+
 ## [0.28.1] - 2022-03-02
 
 ### Added

VERSION

@@ -1 +1 @@
-0.28.1
+0.28.2

doc/source/tutorials/machine_learning/mnist_classifier.ipynb

@@ -6,18 +6,28 @@
    "source": [
     "# MNIST classifier tutorial\n",
     "\n",
-    "MNIST database of handwritten digits is a popular dataset to demonstrate machine learning classifiers. The dataset is comprised of handwritten digits with the corresponding ground truth label. In this tutorial (based on [1]), we train a basic Neural Network (NN) classifier using PyTorch. Once the classifier training workflow has been defined, it is easily converted into a Covalent workflow. This workflow can then be \"dispatched\" for execution and the task completion status / details tracked in the Covalent user interface (UI)."
+    "MNIST database of handwritten digits is a popular dataset to demonstrate machine learning classifiers. The dataset is comprised of handwritten digits with the corresponding ground truth label. In this tutorial (based on [1]), we train a basic Neural Network (NN) classifier using PyTorch. \n",
+    "\n",
+    "A sample of the MNIST dataset:\n",
+    "\n",
+    "<div align=\"center\">\n",
+    "<img src=\"././mnist_images/mnist_examples.png\" style=\"width: 45%; height: 45%\"/>\n",
+    "</div>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A sample of the MNIST dataset:\n",
     "\n",
-    "<div align=\"center\">\n",
-    "<img src=\"././mnist_images/mnist_examples.png\" style=\"width: 45%; height: 45%\"/>\n",
-    "</div>\n"
+    "\n",
+    "The tutorial has two main parts: \n",
+    "\n",
+    "- First, a \"normal\" workflow function (without using Covalent) is defined to train the MNIST classifier.\n",
+    "\n",
+    "- Second, this workflow is converted into a Covalent workflow, which is then \"dispatched\" for execution. \n",
+    "\n",
+    "Lastly, we review the key benefits that are unlocked when transforming a \"normal\" workflow with Covalent. One major advantage of a Covalent workflow is that the task dependencies and execution details can be tracked easily in the Covalent user interface (UI)."
    ]
   },
   {
@@ -90,7 +100,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Construct MNIST classifier training workflow\n",
+    "### Construct MNIST classifier training workflow (without Covalent)\n",
     "\n",
     "Construct a convolutional neural network model by inheriting from `torch.nn.Module`."
    ]
@@ -354,7 +364,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Run MNIST classifier workflow as a normal function\n",
+    "#### Run MNIST classifier workflow as a normal function (without Covalent)\n",
     "\n",
     "Run the MNIST classifier workflow to benchmark the performance and the time taken to train and test the model."
    ]
@@ -426,7 +436,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Run workflow with Covalent\n",
+    "### Transform and run workflow with Covalent\n",
     "\n",
     "First, we convert the normal workflow function into a Covalent workflow function.\n",
     "\n",