Andrej karpathy phd thesis
you insane you know you've hit on something interesting! I had a stack of 4 papers I had reviewed over the last several hours and she picked them up, flipped through each one for 10 seconds, and said one of them was good and the other three bad. Kinect Fusion, 3D meshes, point cloud features) and video things). The good news is that backpropagation is not that difficult to understand, if presented properly. Your accomplishments will have your name attached to them. Youll have to learn how to endow your papers with the same gestalt because many researchers rely on it as a cognitive shortcut when they judge your work. It is an exciting time to be in the field! See a longer explanation in CS231n lecture video. ConvNetJS - a JS library for training Deep Learning models (mainly Neural Networks) entirely in your browser. Do things others dont do but should.
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These are incremental, gross terms that will certainly get your paper rejected. Just to list a few: They can be trained online, they are efficient at test time (in both space and time they are modular (the gradient is possible to derive locally and decomposes very simply through chain rule they are simple, and they work. This is incorrect, and should only be a second or third degree design criterion. Most good conferences have an acceptance rate of about 25 so most papers youll review are bad, which will allow you to build a powerful binary classifier. For instance, youll sometimes hear academics talk about incremental work (this is the worst adjective possible in academia). A representative paper that is relevant to my current interests at Stanford is my nips paper with Adam Coates on training an unsupervised deep learning model on millions of images from and automatically discovering frequently recurring visual concepts.
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