Troubleshooting and Improving Neural Network Performance

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Dhiraj Kumar

1:32:43

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1. Course Overview

1. Course Overview.mp4

01:38

2. Diagnostic Tools - TensorBoard, Model Visualizations, and More

1. Diagnostic Tools TensorBoard.mp4

02:54

2. TensorBoard for Visualizing Models.mp4

02:21

3. Visualizing Neural Network Architectures.mp4

02:39

4. Demo - TensorBoard.mp4

05:24

5. Inspecting Model Weights.mp4

02:19

6. Diagnostic Tools for Identifying Bottlenecks.mp4

02:11

7. Ethical Considerations.mp4

02:02

3. Identifying Common Issues - Overfitting, Underfitting, and Stagnant Learning

1. Common Issues in Neural Network Perfor.mp4

02:46

2. Impacts of Overfitting and Underfittin.mp4

01:56

3. Stagnant Learning and Training Plateau.mp4

01:51

4. Factors Contributing to Overfitting.mp4

01:52

5. Demo - Overfitting.mp4

03:25

6. Mitigating, Overfitting, Underfitting,.mp4

02:11

7. Real-world Scenarios.mp4

02:17

4. Improving Training Stability - Learning Rate Scheduling and Early Stopping

1. Enhancing Training Stability.mp4

02:55

2. Learning Rate and Its Impact.mp4

02:51

3. Learning Rate Scheduling.mp4

01:49

4. Early Stopping to Prevent.mp4

02:04

5. Demo - Learning Rate Scheduling and Early.mp4

04:27

6. Dynamic Learning Rate.mp4

02:20

7. Trade Offs and Best Practices.mp4

02:49

5. Model Interpretability - Understanding Decision Making in Neural Networks

1. Model Interpretability in Neural Networks.mp4

03:12

2. Model Interpretability Relevance.mp4

01:44

3. Techniques for Interpreting.mp4

01:23

4. Transparent and Explainable AI.mp4

02:11

5. Demo - Feature Importance.mp4

04:30

6. Case Studies Showcasing Model Interpretabi.mp4

02:46

7. Ethical Considerations.mp4

02:02

6. Feedback Loops - Continuously Learning and Adapting Models in Production

1. Feedback Loops.mp4

02:59

2. Continuous Learning.mp4

02:00

3. Collecting Feedback Data.mp4

02:15

4. Model Retraining Fine-tuning.mp4

03:06

5. Demo - Template Code for Continuous Learnin.mp4

03:08

6. Real-world Use Cases.mp4

02:15

7. Challenges in Implementing.mp4

02:11

Description

This course will teach you neural network troubleshooting and performance tuning from a data scientist's perspective.

What You'll Learn?

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.

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Machine Learning

Artificial Intelligence

Neural Networks

Dhiraj Kumar

Instructor's Courses

Dhiraj is a Data Evangelist, Technical Writer, and Online Trainer in Python, Machine Learning, Deep Learning, and Artificial Intelligence, C#, ASP.NET, AZURE, AWS.

Pluralsight

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Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.