理解訓練深層前饋神經網絡的難度（Undetanding the difficulty of training deep feedforward neural networks ）...
譯者按：大神bengio 的經典論文之一，不多說 作者：Xavier Glorot Yoshua Bengio 加拿大魁北克 蒙特利爾大學 摘要：在2006年以前，似乎深度多層的神經網絡沒有被成功訓練過。自那以後少數幾種算法顯示成功地訓練了它們，實驗結果表明了深度多層的架構的優越性。所有這些實驗結果都是通過新的初始化或訓練機制獲得的。我們的目標是更好地理解爲什麼隨機初始化的標準梯度下
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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. 神經網絡剖析激活函數優缺點（Undetanding the difficulty of training deep feedforward neural networks）
- 5. Paper之DL之BP：《Understanding the difficulty of training deep feedforward neural networks》
- 6. On the difficulty of training Recurrent Neural Networks
- 7. [論文筆記] [2010] Understanding the Difficulty of Training Deep Feedforward Neural Networks
- 8. Deep Learning：深度前饋神經網絡（四）
- 9. 神經網絡不同激活函數比較--讀《Understanding the difficulty of training deep feedforward neural networks》
- 10. Neural Networks and Deep Learning（week4）深層神經網絡（Deep Neural Networks）