Introduction

The goal of this task is to design and implement an educational framework that enhances your understanding of machine learning (ML) concepts in the context of GIS-based crop trait analysis. The framework employs a novel, visualization-driven approach using TensorBoard to provide real-time insights into key ML processes such as data preparation, model training, feature selection, and prediction evaluation. By integrating interactive visualization tools, this task aims to bridge the gap between theoretical knowledge and practical application, making complex ML processes intuitive and engaging.

Before You Begin

In this section, you will be revisiting code that you and your team utilized in sections one and two . The idea now is that you will be using a framework called TensorBoard. This framework employs a novel, visualization-driven approach to provide real-time insights into key ML processes such as data preparation, model training, feature selection, and prediction evaluation. By integrating interactive visualization tools, this task aims to bridge the gap between theoretical knowledge and practical application, making the complex ML processes that you have already seen more intuitive and engaging.

To download the files needed for the section, please see below:

Download Project Files

• Access the project files here: Project Files.
• You will be looking for the folder labeled Section 3 that contains the following: (TensorBoard_with_QGIS_and_Python.ipynb & TensorBoard_with_Machine_Learning_Regression_Models.ipynb), in it.
• Each of these files, is code that you have previously seen, but now with the addition of the TensorBoard framework. When you run these files, you should now see additional results, than the first time you ran the code blocks.

Utilizing TensorBoard with code from sections one and two will help you to visualize critical aspects of ML, including loss curves, accuracy metrics, feature importance, and geospatial heatmaps. This will help enable you to understand how models learn and perform.

Feel free to rerun the previous sections codes, now with the addition of the TensorBoard framework, and see what changes you notice. Are any new insights provided?

Setting Up Your Own Project

Explore Further with TensorBoard

• If you’d like to learn more and set up your own TensorBoard project, check out these official guides for TensorFlow and PyTorch. They will help you understand how to visualize your models’ performance, track metrics, and gain deeper insights during training.