3D Convolutional Neural Network input shape
I'm having a problem feeding a 3D CNN using Keras and Python to classify 3D shapes. I have a folder with some models in JSON format. I read those models into a Numpy Array. The models are 25*25*25 and represent the occupancy grid of the voxelized model (each position represents if the voxel in position (i,j,k) has points in it or no), so I only have 1 channel of input, like grayscale images in 2D
Is it possible to speed up a quicksort with par in Haskell?
I have got this seemingly trivial parallel quicksort implementation, the code is as follows: import System.Random import Control.Parallel import Data.List quicksort :: Ord a => [a] -> [a] quicksort xs = pQuicksort 16 xs -- 16 is the number of sparks used to sort -- pQuicksort, parallelQuicksort -- As long as n > 0 evaluates the lower and upper part of the list in parallel, -- when we have recursed
What are advantages of Artificial Neural Networks over Support Vector Machines?
As it currently stands, this question is not a good fit for our Q&A format. We expect answers to be supported by facts, references, or expertise, but this question will likely solicit debate, arguments, polling, or extended discussion. If you feel that this question can be improved and possibly reopened, visit the help center for guidance. ANN (Artificial Neural Networks) and SVM (Support Vector M
Convolutional Neural Network (CNN) for Audio
I have been following the tutorials on DeepLearning.net to learn how to implement a convolutional neural network that extracts features from images. The tutorial are well explained, easy to understand and follow. I want to extend the same CNN to extract multi-modal features from videos (images + audio) at the same time. I understand that video input is nothing but a sequence of images (pixel inten
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Implement word2vec in Keras
Why Arrays.sort is quicksort algorithm, why not another sort algorithm?
