Refactor TensorFlow GCP learning path documentation for clarity; update section titles and improve descriptions across multiple files.

Author: madeline-underwood

content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/background.md

@@ -1,5 +1,5 @@
 ---
-title: Getting started with TensorFlow on Google Axion C4A (Arm Neoverse-V2)
+title: Get started with TensorFlow on Google Axion C4A
 
 weight: 2
 
@@ -22,8 +22,6 @@ With support for neural networks, natural language processing (NLP), and compute
 
 To learn more, visit the [official TensorFlow website](https://www.tensorflow.org/).
 
-## Recap and next steps
+## Next steps
 
-You’ve learned about Google Axion C4A Arm-based VMs and the fundamentals of TensorFlow. These technologies provide a powerful, cost-effective foundation for running machine learning workloads in the cloud.
-
-You’re ready to provision your own Arm-based VM and start building and benchmarking TensorFlow models on Google Cloud. Great job—let’s get started!
+Now that you understand Google Axion C4A Arm-based VMs and TensorFlow fundamentals, you can provision your own VM and start benchmarking machine learning workloads on Google Cloud.

content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/baseline.md

@@ -1,5 +1,5 @@
 ---
-title: Test TensorFlow baseline performance on Google Axion C4A Arm virtual machines
+title: Test TensorFlow baseline performance on Google Axion C4A 
 weight: 5
 
 ### FIXED, DO NOT MODIFY
@@ -10,7 +10,7 @@ layout: learningpathall
 
 This section helps you confirm that TensorFlow is installed and working correctly on your Google Axion C4A Arm virtual machine (VM). You'll run tests to check that your CPU can perform TensorFlow operations and basic neural network training.
 
-### Check available devices
+## Check available devices
 
 List the hardware devices TensorFlow can use, such as CPU or GPU. On most VMs, only the CPU is available:
 
@@ -24,7 +24,7 @@ The output is similar to:
 [PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU')]
 ```
 
-### Run a computation test
+## Run a computation test
 
 Multiply two large matrices to verify TensorFlow computations on your CPU and measure execution time:
 
@@ -43,9 +43,9 @@ Computation time: 0.008263111114501953 seconds
 
 This provides a baseline measurement for CPU performance.
 
-### Test neural network execution
+## Test neural network execution
 
-Create a new file named `test_nn.py` to test a simple neural network:
+Create a file named `test_nn.py` with the following code:
 
 ```python
 import keras
@@ -71,7 +71,7 @@ model.fit(x, y, epochs=1, batch_size=32)
 
 This script creates and trains a basic neural network using random data to verify that TensorFlow's deep learning functions work on the Arm platform.
 
-### Run the neural network test
+## Run the neural network test
 
 Execute the script:
 
@@ -85,4 +85,4 @@ TensorFlow displays training progress similar to:
 32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.1024
 ```
 
-You have now verified that TensorFlow is working on your Arm-based VM and can perform both basic computations and neural network training. Great job—your environment is ready for benchmarking!
+TensorFlow is working correctly on your Arm-based VM for both basic computations and neural network training. Your environment is ready for benchmarking.

content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/benchmarking.md

@@ -8,11 +8,11 @@ layout: learningpathall
 
 ## Benchmark TensorFlow models
 
-This section benchmarks multiple TensorFlow models—ResNet50, MobileNetV2, and InceptionV3—using dummy input data. You'll measure average inference time and throughput for each model running on the CPU of your Arm-based Google Cloud Platform (GCP) VM.
+This section benchmarks multiple TensorFlow models - ResNet50, MobileNetV2, and InceptionV3 - using dummy input data. You'll measure average inference time and throughput for each model running on the CPU of your Arm-based Google Cloud Platform (GCP) VM.
 
 TensorFlow Keras (`tf.keras`) is TensorFlow's high-level API for building, training, and benchmarking deep learning models. It provides access to predefined architectures such as ResNet, MobileNet, and Inception, making it easy to evaluate model performance on different hardware setups.
 
-### Activate your virtual environment
+## Activate your virtual environment
 
 Enable your isolated Python environment where TensorFlow is installed:
 
@@ -23,7 +23,7 @@ python -c "import tensorflow as tf; print(tf.__version__)"
 
 This ensures that all TensorFlow-related packages run in a clean, controlled setup without affecting system-wide Python installations.
 
-### Install required packages
+## Install required packages
 
 Install TensorFlow and NumPy for model creation and benchmarking:
 
@@ -33,7 +33,7 @@ pip install tensorflow==2.20.0 numpy
 
 These packages are likely already installed from previous steps. NumPy supports efficient numerical operations, while TensorFlow handles deep learning workloads.
 
-### Create the benchmark script
+## Create the benchmark script
 
 Use an editor to create a Python script named `tf_cpu_benchmark.py` that will run TensorFlow model benchmarking tests.
 
@@ -74,7 +74,7 @@ for name, constructor in models.items():
 
 This script creates model instances without pretrained weights for fair CPU testing, generates random image data for inference, includes a warm-up phase to stabilize model performance, and measures inference time over 50 runs to calculate average performance and throughput.
 
-### Run the benchmark
+## Run the benchmark
 
 Execute the benchmarking script:
 
@@ -98,11 +98,11 @@ InceptionV3 average inference time per batch: 0.8971 seconds
 InceptionV3 throughput: 35.67 images/sec
 ```
 
-### Understand the results
+## Interpret the results
 
 The benchmark provides key performance metrics. Average inference time per batch measures how long it takes to process one batch of input data, with lower values indicating faster performance. Throughput shows how many images the model can process per second, with higher values indicating better efficiency.
 
-### Performance summary
+## Performance summary
 
 The following table shows results from running the benchmark on a `c4a-standard-4` (4 vCPU, 16 GB memory) aarch64 VM in Google Cloud Platform (GCP) using SUSE Linux Enterprise Server (SLES):
 
@@ -112,6 +112,6 @@ The following table shows results from running the benchmark on a `c4a-standard-
 | MobileNetV2 | 0.2909                                     | 110.02                  |
 | InceptionV3 | 0.8971                                     | 35.67                   |
 
-The results demonstrate strong performance for lightweight convolutional neural networks (CNNs) like MobileNetV2, achieving over 110 images/sec on the aarch64 platform. Medium-depth models like InceptionV3 maintain balanced performance between accuracy and latency. Heavier architectures such as ResNet50 show longer inference times but deliver stable throughput, confirming that TensorFlow workloads run efficiently on Arm processors and provide a cost-effective alternative for AI inference tasks.
+The results show strong performance for lightweight CNNs like MobileNetV2, achieving over 110 images/sec on the aarch64 platform. Medium-depth models like InceptionV3 maintain balanced performance between accuracy and latency. Heavier architectures such as ResNet50 show longer inference times but deliver stable throughput.
 
-You have successfully benchmarked TensorFlow models on your Arm-based VM. This demonstrates the efficiency and scalability of Arm platforms for deep learning workloads—great job!
+You have successfully benchmarked TensorFlow models on your Arm-based VM. This demonstrates the efficiency and scalability of Arm platforms for deep learning workloads - great job!

content/learning-paths/servers-and-cloud-computing/tensorflow-gcp/installation.md

@@ -46,7 +46,7 @@ python3.11 -m venv tf-venv
 source tf-venv/bin/activate
 ```
 
-This creates and activates a virtual environment named `tf-venv` to prevent package conflicts.
+Your virtual environment `tf-venv` is now active and isolated from system packages.
 
 ## Upgrade pip