[論文筆記] [2010] Understanding the Difficulty of Training Deep Feedforward Neural Networks
這篇論文主要是從參數初始化和激活函數的角度，通過實驗中觀察網絡在訓練迭代時每層的 activations 和 gradients，來探究訓練深層模型困難的原因，並提出了一種新的參數初始化方式來加快模型訓練時的收斂。 Effect of Activation Functions and Saturation During Training sigmoid 激活函數在之前已經被證明會減慢學習的速度，如
相關文章
- 1. Xavier——Understanding the difficulty of training deep feedforward neural networks
- 2. 【Deep Learning】筆記：Understanding the difficulty of training deep feedforward neural networks
- 3. 論文解析-《Understanding the difficulty of training deep feedforward neural networks》
- 4. Paper之DL之BP：《Understanding the difficulty of training deep feedforward neural networks》
- 5. Understanding Neural Networks Through Deep Visualization 論文筆記
- 6. CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes2018—論文筆記
- 7. 【論文筆記】Training Very Deep Networks - Highway Networks
- 8. Machine Learning & Deep Learning 論文閱讀筆記
- 9. 神經網絡不同激活函數比較--讀《Understanding the difficulty of training deep feedforward neural networks》
- 10. 論文閱讀筆記（五十三）：Understanding Deep Convolutional Networks
- 更多相關文章...
- • CAP理論是什麼？ - NoSQL教程
- • XSLT
- • Tomcat學習筆記（史上最全tomcat學習筆記）
- • Scala 中文亂碼解決
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. Xavier——Understanding the difficulty of training deep feedforward neural networks
- 2. 【Deep Learning】筆記：Understanding the difficulty of training deep feedforward neural networks
- 3. 論文解析-《Understanding the difficulty of training deep feedforward neural networks》
- 4. Paper之DL之BP：《Understanding the difficulty of training deep feedforward neural networks》
- 5. Understanding Neural Networks Through Deep Visualization 論文筆記
- 6. CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes2018—論文筆記
- 7. 【論文筆記】Training Very Deep Networks - Highway Networks
- 8. Machine Learning & Deep Learning 論文閱讀筆記
- 9. 神經網絡不同激活函數比較--讀《Understanding the difficulty of training deep feedforward neural networks》
- 10. 論文閱讀筆記（五十三）：Understanding Deep Convolutional Networks