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Become A Champion In Deep Learning Through This Course
- A framework that you can use to systematically diagnose and improve the performance of your deep learning model.
- Batch size can be used to control the precision of the estimated error and the speed of learning during training.
- Learning rate schedule can be used to fine tune the model weights during training.
- Batch normalization can be used to dramatically accelerate the training process of neural network models.
- Weight regularization will penalize models based on the size of the weights and reduce overfitting.
- Adding noise will make the model more robust to differences in input and reduce overfitting
- Early stopping will halt the training process at the right time and reduce overfitting.
- This is just the beginning of your journey with deep learning performance improvement. Keep practicing and developing your skills.