Neural network architecture design is one of the key hyperparameters in solving problems using deep learning and computer vision. Various neural networks are compared on two key factors i.e. accuracy and computational requirement. In general, as we aim to design more accurate neural networks, the computational requirement increases. In this post, we shall learn about the search for more accurate neural network architectures without worrying about computational need. We shall also see how neural networks can be taught to design themselves and how this technique is being used to discover better neural network architectures(AutoML or Neural Architecture Search).
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