Merge pull request #38 from maxpkatz/hpc-ai-cfd_grammar_part_2

Grammar fixes for hpc_ai CFD part_2 notebook

hpc_ai/ai_science_cfd/English/python/jupyter_notebook/Intro_to_DL/Part_2.ipynb

@@ -25,21 +25,21 @@
    "metadata": {},
    "source": [
     "\n",
-    "In this notebook, participants will be introduced to CNN, implement it using Keras. For an absolute beginner this notebook would serve as a good starting point.\n",
+    "In this notebook you will be introduced to the concept of a convolutional neural network (CNN) and implement one using Keras. This notebook is designed as a starting point for absolute beginners to deep learning.\n",
     "\n",
-    "**Contents of the this notebook:**\n",
+    "**Contents of this notebook:**\n",
     "\n",
-    "- [How a Deep Learning project is planned ?](#Machine-Learning-Pipeline)\n",
-    "- [Wrapping things up with an example ( Classification )](#Wrapping-Things-up-with-an-Example)\n",
-    "     - [Fully Connected Networks](#Image-Classification-on-types-of-Clothes)\n",
+    "- [How a deep learning project is planned](#Machine-Learning-Pipeline)\n",
+    "- [Wrapping things up with an example (classification)](#Wrapping-Things-up-with-an-Example)\n",
+    "     - [Fully connected networks](#Image-Classification-on-types-of-Clothes)\n",
     "\n",
     "\n",
-    "**By the end of this notebook participant will:**\n",
+    "**By the end of this notebook the participant will:**\n",
     "\n",
-    "- Understand the Machine Learning Pipeline\n",
-    "- Write a Deep Learning Classifier and train it.\n",
+    "- Understand machine learning pipelines\n",
+    "- Write a deep learning classifier and train it\n",
     "\n",
-    "**We will be building a _Multi-class Classifier_ to classify images of clothing to their respective classes**"
+    "**We will be building a _multi-class classifier_ to classify images of clothing into their respective classes.**"
    ]
   },
   {
@@ -48,29 +48,29 @@
    "source": [
     "## Machine Learning Pipeline\n",
     "\n",
-    "During the bootcamp we will be making use of the following buckets to help us understand how a Machine Learning project should be planned and executed: \n",
+    "During the bootcamp we will be making use of the following concepts to help us understand how a machine learning (ML) project should be planned and executed:\n",
     "\n",
     "1. **Data**: To start any ML project we need data which is pre-processed and can be fed into the network.\n",
-    "2. **Task**: There are many tasks present in ML, we need to make sure we understand and define the problem statement accurately.\n",
-    "3. **Model**: We need to build our model, which is neither too deep and taking a lot of computational power or too small that it could not learn the important features.\n",
-    "4. **Loss**: Out of the many _loss functions_ present, we need to carefully choose a _loss function_ which is suitable for the task we are about to carry out.\n",
-    "5. **Learning**: As we mentioned in our last notebook, there are a variety of _optimisers_ each with their advantages and disadvantages. So here we choose an _optimiser_ which is suitable for our task and train our model using the set hyperparameters.\n",
-    "6. **Evaluation**: This is a crucial step in the process to determine if our model has learnt the features properly by analysing how it performs when unseen data is given to it. "
+    "2. **Task**: There are many possible tasks in the field of ML; we need to make sure we understand and define the problem statement accurately.\n",
+    "3. **Model**: We need to build our model, which is neither too deep (requiring a lot of computational power) nor too small (preventing it from learning the important features).\n",
+    "4. **Loss**: Out of the many _loss functions_ that can be defined, we need to carefully choose one which is suitable for the task we are about to carry out.\n",
+    "5. **Learning**: There are a variety of _optimisers_, each with their advantages and disadvantages. We must choose one which is suitable for our task and train our model using some suitably chosen hyperparameters.\n",
+    "6. **Evaluation**: We must determine if our model has learned the features properly by analysing how it performs on data it has not previously seen. "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Here we will be building a _Multi-class Classifier_ to classify images of clothing to their respective classes.**\n",
+    "**Here we will be building a _multi-class classifier_ to classify images of clothing into their respective classes.**\n",
     "\n",
-    "We will follow the above discussed pipeline to complete the example.\n",
+    "We will follow the pipeline presented above to complete the example.\n",
     "\n",
-    "## Image Classification on types of clothes  \n",
+    "## Image classification on types of clothes  \n",
     "\n",
-    "####  Step -1 : Data \n",
+    "####  Step 1: Data \n",
     "\n",
-    "We will be using the **F-MNIST ( Fashion MNIST )** dataset, which is a very popular dataset. This dataset contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels).\n",
+    "We will be using the **Fashion MNIST** dataset, which is a very popular introductory dataset in deep learning. This dataset contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 by 28 pixels).\n",
     "\n",
     "<img src=\"images/fashion-mnist.png\" alt=\"Fashion MNIST sprite\"  width=\"600\">\n",
     "\n",
@@ -196,27 +196,27 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Print Array Size of Training Set \n",
-    "print(\"Size of Training Images :\"+str(train_images.shape))\n",
-    "#Print Array Size of Label\n",
-    "print(\"Size of Training Labels :\"+str(train_labels.shape))\n",
-    "\n",
-    "#Print Array Size of Test Set \n",
-    "print(\"Size of Test Images :\"+str(test_images.shape))\n",
-    "#Print Array Size of Label\n",
-    "print(\"Size of Test Labels :\"+str(test_labels.shape))\n",
-    "\n",
-    "#Let's See how our Outputs Look like \n",
-    "print(\"Training Set Labels :\"+str(train_labels))\n",
-    "#Data in the Test Set\n",
-    "print(\"Test Set Labels :\"+str(test_labels))"
+    "# Print array size of training dataset\n",
+    "print(\"Size of Training Images: \" + str(train_images.shape))\n",
+    "# Print array size of labels\n",
+    "print(\"Size of Training Labels: \" + str(train_labels.shape))\n",
+    "\n",
+    "# Print array size of test dataset\n",
+    "print(\"Size of Test Images: \" + str(test_images.shape))\n",
+    "# Print array size of labels\n",
+    "print(\"Size of Test Labels: \" + str(test_labels.shape))\n",
+    "\n",
+    "# Let's see how our outputs look\n",
+    "print(\"Training Set Labels: \" + str(train_labels))\n",
+    "# Data in the test dataset\n",
+    "print(\"Test Set Labels: \" + str(test_labels))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Data Pre-processing\n"
+    "## Data Preprocessing\n"
    ]
   },
   {
@@ -236,7 +236,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The image pixel values range from 0 to 255. Let us now normalise the data range from 0 - 255 to 0 - 1 in both the *Train* and *Test* set. This Normalisation of pixels helps us by optimizing the process where the gradients are computed."
+    "The image pixel values range from 0 to 255. Let us now normalise the data range from 0 - 255 to 0 - 1 in both the *Train* and *Test* set. This normalisation of pixels helps us by optimizing the process where the gradients are computed."
    ]
   },
   {
@@ -255,7 +255,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Let's Print to Veryify if the Data is of the correct format.\n",
+    "# Let's print to verify whether the data is of the correct format.\n",
     "plt.figure(figsize=(10,10))\n",
     "for i in range(25):\n",
     "    plt.subplot(5,5,i+1)\n",
@@ -273,13 +273,13 @@
    "source": [
     "## Defining our Model\n",
     "\n",
-    "Our Model has three layers :\n",
+    "Our model has three layers:\n",
     "\n",
-    "- 784 Input features ( 28 * 28 ) \n",
-    "- 128 nodes in hidden layer (Feel free to experiment with the value)\n",
-    "- 10 output nodes to denote the Class\n",
+    "- 784 input features (28 * 28)\n",
+    "- 128 nodes in the hidden layer (feel free to experiment with this value)\n",
+    "- 10 output nodes to denote the class\n",
     "\n",
-    "Implementing the same in Keras ( Machine Learning framework built on top of Tensorflow, Theano, etc..) \n"
+    "We will implement this model in Keras (TensorFlow's high-level API for machine learning).\n"
    ]
   },
   {
@@ -346,7 +346,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.fit(train_images, train_labels ,epochs=5)"
+    "model.fit(train_images, train_labels, epochs=5)"
    ]
   },
   {
@@ -364,7 +364,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Evaluating the Model using the Test Set\n",
+    "# Evaluating the model using the test dataset\n",
     "\n",
     "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
     "\n",
@@ -375,14 +375,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We get an Accuracy of 87% in the Test dataset which is less than the 89% we got during the Training phase, This problem in ML is called as Overfitting, and we have discussed the same in the previous notebook. \n",
+    "We get an accuracy of 87% on the test dataset, which is less than the 89% we got during the training phase. This problem in machine learning is called overfitting.\n",
     "\n",
     "## Exercise\n",
     "\n",
-    "Try adding more dense layers to the network above and observe change in accuracy.\n",
+    "Try adding more dense layers to the network above and observe the change in accuracy.\n",
     "\n",
     "## Important:\n",
-    "<mark>Shutdown the kernel before clicking on “Next Notebook” to free up the GPU memory.</mark>\n",
+    "<mark>Shut down the kernel before clicking on “Next Notebook” to free up the GPU memory.</mark>\n",
     "\n",
     "\n",
     "## Licensing\n",