Learning representations by back-propagating errors
这篇文章清晰地阐释了backpropagation的过程。 backpropagation的精髓就在于chain rule。同时利用神经网络的结构，使得gradient descent的过程就如同反向的神经网络一般。 假设我们有如下一个神经网络 其中 最终的loss function被定义为 gradient descent所做的事情就是不断调整W的取值，使得最终的loss function最小化
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相关文章
- 1. some understandings about 《Learning representations by back-propagating errors》
- 2. 【笔记】Learning representations by back-propagating errors
- 3. Learning representations by back-propagating errors【译文】
- 4. DIM（Learning deep representations by mutual information estimation and maximization）
- 5. 持续学习-Towards reusable network components by learning compatible representations-arxiv2020
- 6. Person Re-Identification by Deep Learning Multi-Scale Representations 论文笔记
- 7. 【论文笔记——DIM】Learning Deep Representations By Mutual Information Estimation and Maximization
- 8. 【论文阅读】Author2Vec: Learning Author Representations by Combining Content and Link Information
- 9. 【Person Re-ID】Person Re-Identification by Deep Learning Multi-Scale Representations
- 10. ECCV2016【论文笔记】Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles