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A walkthrough in python using Apriori and FP Growth algorithms with the mlxtend library.
We introduce dense vision transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks. We assemble tokens from various stages of the vision transformer into image-like representations at various resolutions and progressively combine them into full-resolution predictions using a convolutional decoder.
The influential Residual Networks designed by He et al. remain the gold-standard architecture in numerous scientific publications. They typically serve as the default architecture in studies, or as baselines when new architectures are proposed. Yet there has been significant progress on best practices for training neural networks since the inception of the ResNet architecture in 2015.
Machine Learning is applied in a variety of fields all over the world. There is no exception in the healthcare industry. Machine Learning can help forecast the existence or absence of motor problems, heart ailments, and other diseases. Such information, if predicted in advance, can provide valuable insights to clinicians, allowing them to tailor their diagnosis and treatment to each individual patient.
It is widely believed that the implicit regularization of stochastic gradient descent (SGD) is fundamental to the impressive generalization behavior we observe in neural networks. In this work, we demonstrate that non-stochastic full-batch training can achieve strong performance on CIFAR-10 that is on-par with SGD, using modern architectures in settings with and without data augmentation.