From the course: Deep Learning with Python: Optimizing Deep Learning Models

Unlock this course with a free trial

Join today to access over 25,500 courses taught by industry experts.

Training a deep learning model using callbacks

Training a deep learning model using callbacks - Python Tutorial

From the course: Deep Learning with Python: Optimizing Deep Learning Models

Start my 1-month free trial Buy for my team

Training a deep learning model using callbacks

“

- [Instructor] In this video, you will learn how to use callbacks to apply early stopping and linear rate scheduling to a deep learning model in Python. I'll be running the code in the 05_07e file. You can follow along by completing the empty code cells in the 05_07b file. Know that this is the third in a three-video sequence that teaches you how to apply batch normalization, gradient clipping, early stopping, and learning rate scheduling to a deep learning model. If you have not done so, watch the previous course videos on how to apply batch normalization to a deep learning model and how to apply gradients clipping to a deep learning model. Those videos provide a detailed explanation of the prior code. Before we begin, let's run the code we created in those videos to get our environment up to speed. So as we've done in the past, the first thing we want to do is select our kernel. You can say Python environments in 3.10. Then I'm going to click on my current code cell and I'm going to…

Contents