In this course, “Troubleshooting and Improving Neural Network Performance - Dhiraj Kumar,” you will learn key concepts of improving the performance of neural networks from a dataset practitioner’s point of view. Over the course’s duration, you will become acquainted with numerous types of troubleshooting techniques developed for neural networks by providing you with the skills required to identify and solve issues efficiently.
First, you will familiarize yourself with the diagnostic tools, focusing on assessing a neural network's performance. From there, strategies will be discovered that can be used to identify other usual issues, such as overfitting, underfitting, and learning stagnation. Last but not least, stability control methods for training will be described, which will facilitate a better and more efficient performance of neural networks.
At the end of the course “Troubleshooting and Improving Neural Network Performance - Dhiraj Kumar,” you will become equipped with practical skills to enable you to gain ideal optimization techniques for a neural network.
Troubleshooting and Improving Neural Network Performance - Dhiraj Kumar Table of Contents:
- Course Overview (1 min)
- Diagnostic Tools: TensorBoard, Model Visualizations, and More (19 mins)
- Identifying Common Issues: Overfitting, Underfitting, and Stagnant Learning (16 mins)
- Improving Training Stability: Learning Rate Scheduling and Early Stopping (19 mins)
- Model Interpretability: Understanding Decision Making in Neural Networks (17 mins)
- Feedback Loops: Continuously Learning and Adapting Models in Production (17 mins)
Who is this course for?
- Impaired academic productivity among learners who wish to learn about neural network problem-solving approaches.
- The target audience consists of students, postgraduates, and researchers who want to start learning the topic of machine learning and neural networks.
- Those who want to know typical problems of stimulating function calculations, including overfitting, underfitting, and learning rate stagnation.
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