理解训练深层前馈神经网络的难度（Undetanding the difficulty of training deep feedforward neural networks ）...
译者按：大神bengio 的经典论文之一，不多说 作者：Xavier Glorot Yoshua Bengio 加拿大魁北克 蒙特利尔大学 摘要：在2006年以前，似乎深度多层的神经网络没有被成功训练过。自那以后少数几种算法显示成功地训练了它们，实验结果表明了深度多层的架构的优越性。所有这些实验结果都是通过新的初始化或训练机制获得的。我们的目标是更好地理解为什么随机初始化的标准梯度下
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相关文章
- 1. Xavier——Understanding the difficulty of training deep feedforward neural networks
- 2. 【Deep Learning】笔记：Understanding the difficulty of training deep feedforward neural networks
- 3. 论文解析-《Understanding the difficulty of training deep feedforward neural networks》
- 4. 神经网络剖析激活函数优缺点（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. [论文笔记] [2010] Understanding the Difficulty of Training Deep Feedforward Neural Networks
- 8. Deep Learning：深度前馈神经网络（四）
- 9. 神经网络不同激活函数比较--读《Understanding the difficulty of training deep feedforward neural networks》
- 10. Neural Networks and Deep Learning（week4）深层神经网络（Deep Neural Networks）