Download Lecture Slides For Chapter 7 of Deep Learning Ian Goodfellow 2016-09-27 PDF Free - Full Version
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About Lecture Slides For Chapter 7 of Deep Learning Ian Goodfellow 2016-09-27
1. Regularization is any modification made to a learning algorithm intended to reduce generalization error without increasing training error. 2. Common regularization techniques for deep learning include weight decay, norm penalties like L1 and L2, dataset augmentation, multi-task learning, early stopping, dropout, and adversarial training. 3. Regularization helps improve generalization by preventing overfitting, encouraging sparsity, introducing invariance, and making the model robust.
Detailed Information
Author: | ['Ricardo Rezende'] |
---|---|
Publication Year: | 2022 |
Pages: | 13 |
Language: | English |
Format: | |
Price: | FREE |
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