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About Lesson 5 Deep Neural Net Optimization Tuning Interpretability
Deep learning models require optimization of hyperparameters to minimize loss and improve performance. Batch normalization standardizes layer outputs during training to improve model stability and convergence. Hyperparameter tuning explores different combinations of hyperparameters, such as learning rate and optimizer type, to find the optimal configuration. Interpretability techniques help explain model predictions to increase trust and address bias concerns.
Detailed Information
| Author: | ['VIJENDHER REDDY GURRAM'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 105 |
| Language: | English |
| Format: | |
| Price: | FREE |
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