【Deep Learning】筆記：Understanding the difficulty of training deep feedforward neural networks
這幾天讀了這篇論文，在這裏將大致內容寫在這裏。 Abstract 介紹這篇論文的主要內容就是嘗試更好的理解爲什麼使用「標準隨機初始化」來計算使用標準梯度下降的網絡效果通常來講都不是很好。 首先研究了不同的非線性激活函數的影響，發現 sigmoid 函數它的均值會導致在隱層中很容易到達函數的飽和區域，因此sigmoid 激活函數在隨機初始化的深度網絡中並不合適。但同時驚喜的發現，處於飽和的神經元能夠
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相關文章
- 1. Xavier——Understanding the difficulty of training deep feedforward neural networks
- 2. 論文解析-《Understanding the difficulty of training deep feedforward neural networks》
- 3. Paper之DL之BP：《Understanding the difficulty of training deep feedforward neural networks》
- 4. [論文筆記] [2010] Understanding the Difficulty of Training Deep Feedforward Neural Networks
- 5. 神經網絡不同激活函數比較--讀《Understanding the difficulty of training deep feedforward neural networks》
- 6. Exploring the teaching of deep learning in neural networks
- 7. Neural Networks and Deep Learning 學習筆記（一）
- 8. Neural Networks and Deep Learning
- 9. On the difficulty of training Recurrent Neural Networks
- 10. 理解訓練深層前饋神經網絡的難度（Undetanding the difficulty of training deep feedforward neural networks ）...