Michael Dzamba

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Deep convolutional neural networks comprise a subclass of deep neural networks (DNN) with a constrained architecture that leverages the spatial and temporal structure of the domain they model. Convolutional networks achieve the best pre-dictive performance in areas such as speech and image recognition by hierarchically composing simple local features into(More)
Overview We've organized our presentation into three stages: 1. A more detailed coverage of the building blocks of CNNs 2. Attempts to explain how and why Residual Networks work 3. Survey extensions to ResNets and other notable architectures Topics covered: ● Alternative activation functions ● Relationship between fully connected layers and convolutional(More)
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