DeepXDE Tutorial #8: Mastering Callbacks to Automate & Visualize Neural Network Training Predictions

Video-ID-V57

Welcome to our DeepXDE tutorial series! In this video tutorial, we dive deep into mastering callbacks in the DeepXDE library and showcase how to implement them effectively to automate and visualize your neural network training process. Using a fourth-order Euler beam problem as an example, we demonstrate how to set up callbacks to save predictions during training and use them to create animations that visualize the model’s training progress. This tutorial is your complete guide to understanding callbacks in DeepXDE.

What you'll learn in this video:
Learn the fundamentals of callbacks in DeepXDE.
Understand how to automate saving epoch level predictions during training.
Explore how to use saved epoch level predictions to generate animations visualizing model improvement.
Gain insights into combining multiple callbacks for enhanced training control.

Timestamps:
00:00 Introduction – Overview of the tutorial and learning objectives
02:31 What Are Callbacks? – Understanding their purpose and use cases
04:00 Euler Beam Problem – Defining prerequisites and Neural Network configuration
07:19 Callbacks – Dive deep
14:11 Generating Animations – Visualizing predictions over training epochs
17:58 Additional callbacks examples and combining multiple custom callback functions
19:19 Closing remarks

Make sure to watch the previous tutorials in the series for a complete understanding of DeepXDE.
Playlist link:    • DeepXDE Masterclass  
Tutorial Series tentative curriculum: https://elastropy.notion.site/Explori...

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Happy Learning! 🎓