Learning representations by back-propagating errors
這篇文章清晰地闡釋了backpropagation的過程。 backpropagation的精髓就在於chain rule。同時利用神經網絡的結構，使得gradient descent的過程就如同反向的神經網絡一般。 假設我們有如下一個神經網絡 其中 最終的loss function被定義爲 gradient descent所做的事情就是不斷調整W的取值，使得最終的loss function最小化
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相關文章
- 1. some understandings about 《Learning representations by back-propagating errors》
- 2. 【筆記】Learning representations by back-propagating errors
- 3. Learning representations by back-propagating errors【譯文】
- 4. DIM（Learning deep representations by mutual information estimation and maximization）
- 5. 持續學習-Towards reusable network components by learning compatible representations-arxiv2020
- 6. Person Re-Identification by Deep Learning Multi-Scale Representations 論文筆記
- 7. 【論文筆記——DIM】Learning Deep Representations By Mutual Information Estimation and Maximization
- 8. 【論文閱讀】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【論文筆記】Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles