https://echo360.org.au/lesson/955bbf1e-48dc-4cda-968f-a9dff443e9d5/classroom#sortDirection=desc
1. ML VS DL ：
1） 传统方法，我们需要针对图片设计 feature extractor去提取Low/Mid/High - level的features，需要大量的计算
2）DL中，自动学习features，do not design features anymore; Images as input, objective functions as Output;
2. NN VS CNN：
1）NN Weight parameters太多了，CNN是用filter
例如 Alex-net中 227*227*3 -> 55*55*96 ，共享了11*11*3 * 96 个weights
如果不共享，则55*55*96中的每一个output都要对应一组 11*11*3的weights ； 55*55*96*11*11*3 =?
2） CNN neurons are arranged in 3 dimensions: Width, Height and Depth.
2）Neurons in a layer are only connected to a small region of the layer before it (hence not fully connected)