From the course: Deep Learning with Python: Optimizing Deep Learning Models
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Continuing to optimize deep learning models - Python Tutorial
From the course: Deep Learning with Python: Optimizing Deep Learning Models
Continuing to optimize deep learning models
- [Instructor] Congrats on completing deep learning with Python, optimizing deep learning models. You've taken a critical step in mastering the techniques needed to fine tune and enhance the performance of deep learning models. By now, you should have a solid understanding of key optimization strategies, including regularization techniques such as lasso, ridge and dropout. Advanced optimization algorithms like RMSprop and Adam. Approaches to hyper parameter tuning and advanced training techniques like batch normalization, early stopping, green clipping, and learning rate scheduling. The knowledge you've acquired in this course will serve as a stepping stone for deeper exploration into the field of AI. So what comes next? Here are some recommendations to continue building your expertise. The best way to solidify your understanding is through practice. Take on projects that challenge you to optimize models for various tasks, such as image classification, text analysis, or time series…
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