(15)[ICML13] Momentum: On the importance of initialization and momentum in deep learning
計劃完成深度學習入門的126篇論文第十五篇，多倫多大學和Google的Ilya Sutskever合做研究一種在梯度降低過程當中優化降低迭代速度的方式，屬於AdaptiveLearning的一種。關聯：Adam，AdaGrad，RMSProp，Adabound。 ABSTRACT&INTRODUCTION 摘要 深度神經網絡(DNNs)和遞歸神經網絡(RNNs)是一種強大的神經網絡模型。在本文中，
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相關文章
- 1. (15)[ICML13] Momentum: On the importance of initialization and momentum in deep learning
- 2. 深度學習:優化方法——momentum、Nesterov Momentum、AdaGrad、Adadelta、RMSprop、Adam
- 3. The Importance of Built-In Cloud Monitoring
- 4. Deep Learning in R
- 5. The Importance of Sync in Embedded
- 6. 」The Importance of Perl 「
- 7. Exploring the teaching of deep learning in neural networks
- 8. Momentum Contrast for Unsupervised Visual Representation Learning
- 9. 【筆記】Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
- 10. momentum