Computer Vision

我喜歡她演講的style，
很有條有理，而且投影片很清晰易懂，
沒有用過多的文字或是理論來轟炸聽者 XD

希望有朝一日我若有機會演講的時候，
也能像她一樣呀。

8:03

In hindsight, this idea of using big data to train computer algorithms may seem obvious now, but back in 2007, it was not so obvious. We were fairly alone on this journey for quite a while. Some very friendly colleagues advised me to do something more useful for my tenure, and we were constantly struggling for research funding. Once, I even joked to my graduate students that I would just reopen my dry cleaner's shop to fund ImageNet. After all, that's how I funded my college years.

• hindsight: 後見之明
• tenure: guaranteed permanent employment

9:40

Now that we have the data to nourish our computer brain, we're ready to come back to the algorithms themselves. As it turned out, the wealth of information provided by ImageNet was a perfect match to a particular class of machine learning algorithms called convolutional neural network, pioneered by Kunihiko Fukushima, Geoff Hinton, and Yann LeCun back in the 1970s and '80s. Just like the brain consists of billions of highly connected neurons, a basic operating unit in a neural network is a neuron-like node. It takes input from other nodes and sends output to others. Moreover, these hundreds of thousands or even millions of nodes are organized in hierarchical layers, also similar to the brain. 

• convolutional neural network: 我好像寫論文的時候有唸過，但因為code不會寫所以我放棄了這個演算法 XD