第一节视频的主要内容：
Fei-Fei Li 女神对Computer Vision的整体介绍。包括了发展历史中的重要事件，其中最为重要的是1959年测试猫视觉神经的实验。
In 1959 Harvard neurophysiologists David H. HubelOffsite Link and Torsten WieselOffsite Link, inserted a microelectrodeOffsite Link into the primary visual cortexOffsite Link of an anesthetized cat.
They then projected patterns of light and dark on a screen in front of the cat, and found that some neuronsOffsite Link fired rapidly when presented with lines at one angle, while others responded best to another angle. They called these neurons "simple cellsOffsite Link."
Still other neurons, which they termed "complex cellsOffsite Link," responded best to lines of a certain angle moving in one direction.
These studies showed how the visual system builds an image from simple stimuli into more complex representations. Many artificial neural networks, fundamental components of deep learningOffsite Link, may be viewed as cascading models of cell types inspired by Hubel and Wiesel's observations.
总结：视觉系统 是信号从简单神经到复杂神经的递进处理，简单神经处理简单信号，复杂神经抽象多个简单神经信号。
后续CNNs有类似的联系。
最后表明整个课程的主要内容：
1、用Python撸CNNs，调试、训练
2、分布式GPU实际应用
3、caffe torch tensorflow
4、RNN DeepDream
附：通关CS231n企鹅群：578975100 validation：DL-CS231n