Auto Encoder vs. PCA (again)

I tried to use Auto Encoder and PCA to do Dimensionality Reduction.
The dataset is from House Prices: Advanced Regression Techniques.
I transformed the data from 79D to 30D, and then reconstruct the data to 70D.
Here’s the result:

As you can see, PCA even did a better job.
And now I come to a conclusion that Auto Encoder is good at seeking different patterns and when fitting a single pattern PCA is a better choice.

Further Plan

After finishing the course “Machine Learning” by Andrew NG, here are two more courses to take:
1. CS231n: Convolutional Neural Networks for Visual Recognition
2. Neural Networks for Machine Learning

And this is the site of Hung-yi Lee: http://speech.ee.ntu.edu.tw/~tlkagk/index.html
There are some good lectures can be read.

—————————— UPD 2017.8.13 ——————————
slides of CS231n can be found here: http://cs231n.stanford.edu/slides/2017/
vedios of CS231n can be found on youtube or bilibili

—————————— UPD 2018.3.4 ——————————
I found this post very useful: https://zhuanlan.zhihu.com/p/25005808