論文筆記2——Dropout：A simple way to prevent neural networks from overfitting
本文主要介紹一種防止過擬合的方法-dropout 模型混合是一種可以用來改善機器學習性能的有效方法，通過將獨立訓練好的模型的輸出求取平均值的方法，但是這種方法的代價是極其昂貴的。這種模型混合的方法是很有效的，當模型有完全不同的結構，且模型的訓練應在不同的數據上進行。要訓練出這惡中結構不同的神經網絡是極其困難的：要爲每個模型找到最優的超參數，同時訓練每個網絡的計算量是巨大的，同時，大的網絡結構需要較
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相關文章
- 1. Dropout:A Simple Way to Prevent Neural Networks from Overfitting
- 2. 【論文精讀】Dropout: A Simple Way to Prevent Neural Networks from Overfitting
- 3. (10) [JMLR14] Dropout: A Simple Way to Prevent Neural Networks from Overfitting
- 4. Dropout: A Simple Way to Prevent Neural Networks from Over tting 論文閱讀
- 5. 譯：《Dropout: A Simple Way to Prevent Neural Networks from Overfitting》
- 6. 【Binarized Neural Networks】論文筆記
- 7. 論文筆記系列-Simple And Efficient Architecture Search For Neural Networks
- 8. 【論文筆記】DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks
- 9. A Beginner's Guide To Understanding Convolutional Neural Networks - Part 2
- 10. Singing-Voice Separation from Monaural Recordings using Deep Recurrent Neural Networks論文筆記