本文是关于李宏毅教授课程中深度自解码器的学习笔记，涉及PCA的扩展、预训练DNN、去噪自解码器、受限玻尔兹曼机和深度信念网络。自解码器在文本检索和相似图像搜索中有应用，通过预训练提升深度神经网络的性能。此外，还讨论了卷积神经网络中的自解码器和序列到序列自解码器在处理序列数据上的优势。
本博客是针对李宏毅教授在youtube上发布的课程视频的学习笔记。
视频地址：ML Lecture 16: Unsupervised Learning - Auto-encoder
- Auto-encoder
- Encoder and Decoder
- Starting from PCA
- Application Text Retrieval
- Application Similar Image Search
- Pre-training DNN
- De-noising auto-encoder contractive auto-encoder
- Restricted Boltzmann Machine RBM -different from DNN just look similar
- Deep Belief Network DBN -different from DNN just look similar
- Auto-encoder for CNN
- Suquence-to-Sequence Auto-encoder
Auto-encoder
Encoder and Decoder
Each one of them can’t be trained respectively(no goal or no input), but they can be linked and trained together.
Starting from PCA
PCA only has one hidden layer, so we can deepen it to Deep Auto-encoder.
Above is Hinton(2006)’s design of deep auto-encoder, it achieves good result.
Application: Text Retrieval
To compress a
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