Grammar fixes for hpc_ai CFD start_here notebook

hpc_ai/ai_science_cfd/English/python/jupyter_notebook/Start_Here.ipynb

@@ -6,15 +6,15 @@
    "source": [
     "# Welcome to AI for Science Bootcamp\n",
     "\n",
-    "The objective of this bootcamp is to give an introduction to application of Artificial Intelligence (AI) algorithms in Science ( High Performance Computing(HPC) Simulations ). This bootcamp will introduce participants to fundamentals of AI and how those can be applied to different HPC simulation domains. \n",
+    "The objective of this bootcamp is to provide an introduction to applications of artificial intelligence (AI) algorithms in scientific high performance computing. This bootcamp will introduce participants to fundamentals of AI and how AI can be applied to HPC simulation domains.\n",
     "\n",
-    "The following contents will be covered during the Bootcamp :\n",
+    "The following contents will be covered during the bootcamp:\n",
     "- [CNN Primer and Keras 101](Intro_to_DL/Part_2.ipynb)\n",
     "- [Steady State Flow using Neural Networks](CFD/Start_Here.ipynb)\n",
     "\n",
     "## Quick GPU Check\n",
     "\n",
-    "Before moving forward let us check if Tensorflow backend is able to see and use GPU"
+    "Before moving forward let us verify that TensorFlow is able to see and use your GPU."
    ]
   },
   {
@@ -45,7 +45,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The output of the cell above should show all the available compaitable GPU on the system. If no GPU device is listed or you see an error means that there was no compaitable GPU present on the system and the future calls may run on CPU consuming more time."
+    "The output of the cell above should show an available compatible GPU on the system (if there are multiple GPUs, only device 0 will be shown). If no GPU device is listed or you see an error, it means that there was no compatible GPU present on the system, and the future calls may run on the CPU, consuming more time."
    ]
   },
   {
@@ -54,15 +54,15 @@
    "source": [
     "## [CNN Primer and Keras 101](Intro_to_DL/Part_2.ipynb)\n",
     "\n",
-    "In this notebook, participants will be introduced to Convolution Neural Network and how to implement one using Keras API. For an absolute beginner to CNN and Keras this notebook would serve as a good starting point.\n",
+    "In this notebook, participants will be introduced to convolutional neural networks (CNNs) and how to implement one using the Keras API in TensorFlow. This notebook would serve as a good starting point for absolute beginners to neural networks.\n",
     "\n",
     "**By the end of this notebook you will:**\n",
     "\n",
-    "- Understand the Machine Learning pipeline\n",
-    "- Understand how a Convolution Neural Network works\n",
-    "- Write your own Deep Learning classifier and train it.\n",
+    "- Understand machine learning pipelines\n",
+    "- Understand how a convolutional neural network works\n",
+    "- Write your own deep learning classifier and train it\n",
     "\n",
-    "For in depth understanding of Deep Learning Concepts, visit [NVIDIA Deep Learning Institute](https://www.nvidia.com/en-us/deep-learning-ai/education/)"
+    "For an in-depth understanding of deep learning concepts, visit the [NVIDIA Deep Learning Institute](https://www.nvidia.com/en-us/deep-learning-ai/education/)."
    ]
   },
   {
@@ -71,24 +71,24 @@
    "source": [
     "## [Steady State Flow using Neural Networks](CFD/Start_Here.ipynb)\n",
     "\n",
-    "In this notebook, participant will be introduced to how Deep Learning can be applied in the field of Fluid Dynamics.\n",
+    "In this notebook, participants will be introduced to how deep learning can be applied in the field of fluid dynamics.\n",
     "\n",
     "**Contents of this notebook:**\n",
     "\n",
     "- Understanding the problem statement\n",
-    "- Building a Deep Learning Pipeline\n",
-    "    - Understand data and task\n",
+    "- Building a deep learning pipeline\n",
+    "    - Understand the data and the task\n",
     "    - Discuss various models\n",
-    "    - Define Neural network parameters\n",
-    "- Fully Connected Networks\n",
+    "    - Define neural network parameters\n",
+    "- Fully connected networks\n",
     "- Convolutional models\n",
     "- Advanced networks\n",
     "\n",
-    "**By the end of the notebook participant will:** \n",
+    "**By the end of the notebook the participant will:** \n",
     "\n",
-    "- Understand the process of applying Deep Learning to Computational Fluid Dynamics\n",
-    "- Understanding how Residual Blocks work.\n",
-    "- Benchmark between different models and how they compare against one another.\n",
+    "- Understand the process of applying deep learning to computational fluid dynamics\n",
+    "- Understand how residual blocks work\n",
+    "- Benchmark between different models and how they compare against one another\n",
     "\n",
     "## Licensing\n",
     "This material is released by NVIDIA Corporation under the Creative Commons Attribution 4.0 International (CC BY 4.0)"