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태그 kon - 이것은 페이지 6 페이지입니다 - GeneraCodice
Understanding how convolutional layers work
https://www.generacodice.com/ko/articolo/2684278/understanding-how-convolutional-layers-work
convolution
-
backpropagation
-
training
-
cnn
datascience.stackexchange
For semantic sementation, why am I getting better loss values with binary cross entropy than dice coef?
https://www.generacodice.com/ko/articolo/2683406/for-semantic-sementation-why-am-i-getting-better-loss-values-with-binary-cross-entropy-than-dice-coef
image-segmentation
-
loss-function
-
cnn
-
semantic-segmentation
datascience.stackexchange
Improving misclassification for one class in a multi-class classification task
https://www.generacodice.com/ko/articolo/2683170/improving-misclassification-for-one-class-in-a-multi-class-classification-task
deep-learning
-
accuracy
-
multilabel-classification
-
keras
-
cnn
datascience.stackexchange
When a dataset is huge, what do you do to train with all the images on i t?
https://www.generacodice.com/ko/articolo/2683118/when-a-dataset-is-huge-what-do-you-do-to-train-with-all-the-images-on-i-t
dataset
-
training
-
cnn
datascience.stackexchange
Is Flatten() layer in keras necessary?
https://www.generacodice.com/ko/articolo/2683043/is-flatten-layer-in-keras-necessary
neural-network
-
convnet
-
keras
-
cnn
-
transfer-learning
datascience.stackexchange
Is it possible to reuse my CNN trained model over a new dataset with different number of classes?
https://www.generacodice.com/ko/articolo/2682982/is-it-possible-to-reuse-my-cnn-trained-model-over-a-new-dataset-with-different-number-of-classes
classification
-
tensorflow
-
keras
-
cnn
-
machine-learning-model
datascience.stackexchange
How do stacked CNN layers work?
https://www.generacodice.com/ko/articolo/2682713/how-do-stacked-cnn-layers-work
machine-learning
-
feature-extraction
-
cnn
datascience.stackexchange
Why are RNNs used in some computer vision problems?
https://www.generacodice.com/ko/articolo/2681041/why-are-rnns-used-in-some-computer-vision-problems
computer-vision
-
deep-learning
-
rnn
-
cnn
datascience.stackexchange
What does it mean when the shape of input images is (600,64,64,3)?
https://www.generacodice.com/ko/articolo/2680963/what-does-it-mean-when-the-shape-of-input-images-is-600-64-64-3
convolution
-
deep-learning
-
deep-network
-
cnn
datascience.stackexchange
Separating styles of numbers for simple digit classification
https://www.generacodice.com/ko/articolo/2679697/separating-styles-of-numbers-for-simple-digit-classification
image-classification
-
cnn
-
mnist
datascience.stackexchange
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결과가 발견되었습니다: 1034