Machine Learning
218 learners
TensorFlow Techniques for Model Optimization
This course delves into advanced TensorFlow techniques to boost model performance and reliability. Learn about using regularization and dropout to prevent overfitting, and explore real-time training improvements with callbacks. Each module is concise and impactful, equipping you with practical skills to enhance your machine learning models.
Python
Scikit-learn
TensorFlow
4 lessons
20 practices
3 hours
Model Validation and Selection
Lessons and practices
Regularization: See it in Action
Modify Regularization Techniques in Model
Fix the Regularization Mistakes
Add L1 and L2 Regularization
Implement Regularization in TensorFlow
Model Summary with Dropout Layers
Adjust Dropout Rate in Model
Fix Model Dropout Issues
Add Dropout Layer to Model
Build a Model with Dropout
Implementing TensorFlow Callbacks
Modifying TensorFlow Callbacks
Debug TensorFlow Callbacks
Completing Callbacks Implementation in TensorFlow
Write TensorFlow Callbacks From Scratch
Bridging Libraries for Evaluation
Adjust Cross Validation Parameters
Fix K-Fold Cross-Validation Bug
Integrate Scikit-Learn with TensorFlow
Bridging TensorFlow and Scikit-Learn
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