Merge pull request #1701 from dominica-of/main

Edge AI with PyTorch & ExecuTorch - Tiny Sentiment Analysis on Arm

Author: pareenaverma

content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_index.md

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+---
+title: Edge AI with PyTorch & ExecuTorch - Tiny Sentiment Analysis on Arm
+
+minutes_to_complete: 90
+
+who_is_this_for: This topic is for machine learning engineers, embedded AI developers, and researchers interested in deploying TinyML models for NLP on Arm-based edge devices using PyTorch and ExecuTorch. 
+
+learning_objectives: 
+    - Train a custom CNN-based sentiment classification model implemented in PyTorch.
+    - Optimize and convert the model using ExecuTorch for Arm-based edge devices.
+    - Deploy and run inference on the Corstone-320 FVP.
+
+prerequisites:
+   - Basic knowledge of machine learning concepts. 
+   - It is advised to complete The Learning Path, [Introduction to TinyML on Arm using PyTorch and ExecuTorch](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm) before starting this learning path. 
+   - Familiarity with Python and PyTorch.
+   - A Linux host machine or VM running Ubuntu 22.04 or higher.
+   - An Arm license to run the examples on the Corstone-320 Fixed Virtual Platform (FVP), for hands-on deployment.  
+
+
+author: Dominica Abena O. Amanfo
+
+### Tags
+skilllevels: Intermediate 
+subjects: ML
+armips:
+    - Cortex-A
+tools_software_languages:
+    - tinyML 
+    - CNN 
+    - PyTorch
+    - ExecuTorch
+    
+operatingsystems:
+    - Linux
+
+
+further_reading:
+    - resource:
+        title: Run Llama 3 on a Raspberry Pi 5 using ExecuTorch 
+        link: /learning-paths/embedded-and-microcontrollers/rpi-llama3
+        type: website
+    - resource:
+        title: ExecuTorch Examples
+        link: https://github.com/pytorch/executorch/blob/main/examples/README.md
+        type: website
+
+
+
+### FIXED, DO NOT MODIFY
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+weight: 1                       # _index.md always has weight of 1 to order correctly
+layout: "learningpathall"       # All files under learning paths have this same wrapper
+learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
+---

content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/_next-steps.md

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+weight: 21                  # Set to always be larger than the content in this path to be at the end of the navigation.
+title: "Next Steps"         # Always the same, html page title.
+layout: "learningpathall"   # All files under learning paths have this same wrapper for Hugo processing.
+---

content/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/env-setup-1.md

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+---
+title: Environment Setup
+weight: 2
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Overview 
+In this course, you will learn how to train and run inference using a Tiny Sentiment Classifier. You'll deploy the model on the Arm Corstone-320 FVP for sentiment analysis. 
+
+We will train a lightweight convolutional neural network (CNN)-based sentiment classifier using synthetic text data. This model is optimized for small devices, using embedding layers and 1D convolutions for efficient text classification.
+
+
+## Environment Setup
+Setup your development environment for TinyML by following the first 3 chapters of the [Introduction to TinyML on Arm using PyTorch and ExecuTorch](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm) Learning Path (LP).
+
+
+If you just followed the LP above, you should already have your virtual environment activated. If not, activate it using: 
+
+```console
+source $HOME/executorch-venv/bin/activate
+```
+The prompt of your terminal now has `(executorch-venv)` as a prefix to indicate the virtual environment is active.
+
+Run the commands below to install the dependencies.
+
+```bash
+pip install argparse json
+```
+You are now ready to build the model
+
+