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    Related websites

    What is the difference between Conv1D and Conv2D?

    Jul 31, 2017 · I will be using a Pytorch perspective, however, the logic remains the same. When using conv1d(), we have to keep in mind that we are most likely going to work with 2-dimensional inputs such as one-hot-encode DNA sequences or black and white pictures.

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    What does 1x1 convolution mean in a neural network?

    $\begingroup$ 1x1 conv creates channel-wise dependencies with a negligible cost. This is especially exploited in depthwise-separable convolutions. This is especially exploited in depthwise-separable convolutions.

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    Where should I place dropout layers in a neural network?

    Oct 14, 2016 · $\begingroup$ I've updated the answer to clarify that in the work by Park et al., the dropout was applied after the RELU on each conv layer. I do not believe they investigated the effect of adding dropout following max pooling layers. $\endgroup$ –

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    Convolutional Layers: To pad or not to pad? - Cross Validated

    Quote from Stanford lectures: "In addition to the aforementioned benefit of keeping the spatial sizes constant after conv, doing this actually improves performance. If the conv layers were to not zero-pad the inputs and only perform valid convolutions, then the size of the volumes would reduce by a small amount after each conv, and the information at the borders would be …

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    In CNN, are upsampling and transpose convolution the same?

    Sep 24, 2019 · Both the terms "upsampling" and "transpose convolution" are used when you are doing "deconvolution" (<-- not a good term, but let me use it here). Originally, I thought that they mean the same t

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    Difference between Conv and FC layers? - Cross Validated

    Nov 9, 2017 · (Note that each conv layer usually learns a set of several filters, each of which gets applied repeatedly across the input. E.g. if the conv layer learns 16 different features, it is said to have a 'depth' of 16.) FC layers are used to detect specific global configurations of the features detected by the lower layers in the net.

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    How to convert fully connected layer into convolutional layer?

    Feb 22, 2017 · In this example, as far as I understood, the converted conv layer should have the shape (7,7,512), meaning (width, height, feature dimension). And we have 4096 filters. And the output of each filter's spatial size can be calculated as (7-7+0)/1 + 1 = 1. Therefore we have a 1x1x4096 vector as output.

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    machine learning - RNN vs Convolution 1D - Cross Validated

    Aug 15, 2018 · Intuitively, are both RNN and 1D conv nets more or less the same? I mean the input shape for both are 3-D tensors, with the shape of RNN being ( batch, timesteps, features) and the shape of 1D conv nets being (batch, steps, channels). They are both used for tasks involving sequences like time series, NLP etc. So my question here is this,

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    What is MBConv that EfficientNetv2 is using? - Cross Validated

    Apr 5, 2021 · EfficinetNetV2 uses MBconv/Fused-MBconv as a part of it's architecture. There is no clarity of what these operations actually are from the paper (nor from the references). It appears that it is som

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    Why do we use ReLU in neural networks and how do we use it?

    Stack Exchange Network. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

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