Troubleshooting and Improving Neural Network Performance

سرفصل های دوره

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

1. Course Overview

1. Course Overview

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

1. Diagnostic Tools TensorBoard

2. TensorBoard for Visualizing Models

3. Visualizing Neural Network Architectures

4. Demo - TensorBoard

5. Inspecting Model Weights

6. Diagnostic Tools for Identifying Bottlenecks

7. Ethical Considerations

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

1. Common Issues in Neural Network Perfor

2. Impacts of Overfitting and Underfittin

3. Stagnant Learning and Training Plateau

4. Factors Contributing to Overfitting

5. Demo - Overfitting

6. Mitigating, Overfitting, Underfitting,

7. Real-world Scenarios

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

1. Enhancing Training Stability

2. Learning Rate and Its Impact

3. Learning Rate Scheduling

4. Early Stopping to Prevent

5. Demo - Learning Rate Scheduling and Early

6. Dynamic Learning Rate

7. Trade Offs and Best Practices

5. Model Interpretability - Understanding Decision Making in Neural Networks

1. Model Interpretability in Neural Networks

2. Model Interpretability Relevance

3. Techniques for Interpreting

4. Transparent and Explainable AI

5. Demo - Feature Importance

6. Case Studies Showcasing Model Interpretabi

7. Ethical Considerations

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

1. Feedback Loops

2. Continuous Learning

3. Collecting Feedback Data

4. Model Retraining Fine-tuning

5. Demo - Template Code for Continuous Learnin

6. Real-world Use Cases

7. Challenges in Implementing

139,000 تومان

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تولید کننده: PluralSight