[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
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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