(10) [JMLR14] Dropout: A Simple Way to Prevent Neural Networks from Overfitting
計劃完成深度學習入門的126篇論文第十篇，多倫多大學的Geoffrey Hinton·和Alex Krizhevsky使用一種新的regularization方法Dropout。 ABSTRACT&INTRODUCTION 摘要 具備大量參數的深度神經網絡是很是強大的機器學習系統。然而，在這樣的網絡中，過擬合是一個嚴重的問題。大型網絡的使用也很緩慢，經過在測試時結合許多不一樣的大型神經網絡的預測，
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相關文章
- 1. (10) [JMLR14] Dropout: A Simple Way to Prevent Neural Networks from Overfitting
- 2. Dropout:A Simple Way to Prevent Neural Networks from Overfitting
- 3. 【論文精讀】Dropout: A Simple Way to Prevent Neural Networks from Overfitting
- 4. 譯：《Dropout: A Simple Way to Prevent Neural Networks from Overfitting》
- 5. Dropout: A Simple Way to Prevent Neural Networks from Over tting 論文閱讀
- 6. 論文筆記2——Dropout：A simple way to prevent neural networks from overfitting
- 7. A Beginner's Guide To Understanding Convolutional Neural Networks - Part 2
- 8. 【轉】A Beginner's Guide To Understanding Convolutional Neural Networks
- 9. A Beginner's Guide To Understanding Convolutional Neural Networks(轉)
- 10. A Beginner's Guide To Understanding Convolutional Neural Networks - Part 1