Stanford University CS231n Convolutional Neural Networks for Visual Recognition

CS231n Convolutional Neural Networks for Visual Recognition. This network is running live in your browser. Images into one of 10 classes and was trained with ConvNetJS. Its exact architecture is conv-relu-conv-relu-poolx3-fc-softmax, for a total of 17 layers and 7000 parameters. It uses 3x3 convolutions and 2x2 pooling regions. By the end of the class, you will know exactly what all these numbers mean. Class Time and Location. Winter quater January - March, 2015. Lecture Monday, Wednesday 215-330.

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Stanford University CS231n Convolutional Neural Networks for Visual Recognition

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CS231n Convolutional Neural Networks for Visual Recognition. This network is running live in your browser. Images into one of 10 classes and was trained with ConvNetJS. Its exact architecture is conv-relu-conv-relu-poolx3-fc-softmax, for a total of 17 layers and 7000 parameters. It uses 3x3 convolutions and 2x2 pooling regions. By the end of the class, you will know exactly what all these numbers mean. Class Time and Location. Winter quater January - March, 2015. Lecture Monday, Wednesday 215-330.

CONTENT

This site cs231n.stanford.edu states the following, "CS231n Convolutional Neural Networks for Visual Recognition." We noticed that the website said " This network is running live in your browser." It also stated " Images into one of 10 classes and was trained with ConvNetJS. Its exact architecture is conv-relu-conv-relu-poolx3-fc-softmax, for a total of 17 layers and 7000 parameters. It uses 3x3 convolutions and 2x2 pooling regions. By the end of the class, you will know exactly what all these numbers mean. Winter quater January - March, 2015. Lecture Monday, Wednesday 215-330."

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