Understand various troubleshooting techniques for neural networks and how to improve neural network performance effectively. In this course, Troubleshooting and Improving Neural Network Performance, you’ll gain the ability to troubleshoot neural network performance effectively. First, you’ll explore diagnostic tools for analyzing neural network performance. Next, you’ll discover how to identify common issues such as overfitting, underfitting, and stagnant learning. Finally, you’ll learn how to improve training stability. When you’re finished with this course, you’ll have the troubleshooting skills needed to improve neural network performance.
Author Name: Dhiraj Kumar
Author Description:
Dhiraj is a Data Scientist and AWS-certified online trainer in Python, machine learning, deep learning, artificial intelligence, Java, C#, ASP.NET, AZURE, and AWS.

Table of Contents

• Course Overview
  1min
• Diagnostic Tools: TensorBoard, Model Visualizations, and More
  19mins
• Identifying Common Issues: Overfitting, Underfitting, and Stagnant Learning
  16mins
• Improving Training Stability: Learning Rate Scheduling and Early Stopping
  19mins
• Model Interpretability: Understanding Decision Making in Neural Networks
  17mins
• Feedback Loops: Continuously Learning and Adapting Models in Production
  17mins