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Applying dropout effectively in CNN

Firstly, why do we use dropout in the first place? Dropout is a regularization technique designed to improve generalization and prevent overfitting. With this in mind, you should not necessarily ...
Karl's user avatar
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Asynchronous Training of Deep Learning Models

By default, CUDA computations (i.e. kernels) are asynchronous (see the CUDA docs and the Pytorch docs). Therefore, what you suggested is already happening under the hoods.
noe's user avatar
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why validation accuracy is stuck at 75%?

My first inclination is that your dataset size is relatively small, and you have many hidden layers, which increases the likelihood of your model overgeneralizing on the training data, leading to ...
Daniel Curtis's user avatar

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