Examples of using Convolutional in English and their translations into Vietnamese
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When to Use Convolutional Neural Networks?
Examples include Hamming code, Reed-Solomon code, Reed-Muller code,Binary Golay code, convolutional code, turbo code and others.
So a convolutional layer consists of several different feature maps.
Why did you use a convolutional neural network?
Convolutional Networks(ConvNets)[1,2] is the deep learning system used in the paper.
Error detection and correction is made by both a convolutional code and Reed-Solomon error correction.
Convolutional Neural Network are a class of deep neural networks, most commonly applied to analysing images.
CDBNs have structure very similar to a convolutional neural networks and are trained similar to deep belief networks.
The convolutional interleaving process shall be based on the Forney approach[2] which is compatible with the Ramsey type III approach, with I= 12.
For symbol protection, a block Reed-Solomon code is used,and for bit protection, a convolutional code with different code rates is employed.
The process relied on a convolutional neural network that pored over 4,396 CT scans.
They capitalize on studies that were done in last couple of years on atype of machine learning known as deep convolutional neural network.
When we hear about convolutional neural networks, we usually think of computer vision.
In July 2015 Google released DeepDream, an image recognition softwarecapable of creating psychedelic images using a convolutional neural network.
When we hear about Convolutional Neural Network(CNNs), we typically think of Computer Vision.
ImageNet was eventually followed by AlexNet, which utilized a deep Convolutional Neural Network architecture that's still used to this day.
Although convolutional coding with Viterbi decoding might be useful in dealing with those other problems, it may not be the best technique.
For the study, published in the journal Icarus,the team first trained the convolutional neural network on a dataset covering two-thirds of the moon.
Algorithms like convolutional neural networks, also known as convnets or CNNs, can handle enormous datasets of images and even learn from them.
This research indicated that the approach based on using convolutional neural networks and methods of deep learning to identify a writer's gender, is the most optimal.
Convolutional neural networks(CNNs) are similar to ordinary neural networks(NNs) in the manner that they are also made up of neurons that have learnable weights and biases.
As of 2011, the state of the art indeep learning feedforward networks alternates convolutional layers and max-pooling layers,[64][65] topped by several fully connected or sparsely connected layer followed by a final classification layer.
Convolutional encoding with Viterbi decoding is a FEC technique that is particularly suited to a channel in which the transmitted signal is corrupted mainly by additive white gaussian noise(AWGN).
In comparison with other deep architectures, convolutional neural networks are starting to show superior results in both image and speech applications.
Every convolutional layer has an additional max pooling. The network is trained to minimize L2 error for predicting the mask ranging over the entire training set containing bounding boxes represented as masks.
This particular neural network has four convolutional layers, seen as the thick bars on the left, and three dense layers, seen as the more narrow bars on the right.
There is a Convolutional Neural Network(CNN) that classifies visual similarities between inputted scenes and a dataset of hundreds of different movies, used to mimic the kind of decision making a film editor makes.
Deep learning techniques will be used totrain multiple neural networks such as convolutional neural networks, deep residual networks, generative query networks or other generative neural networks to achieve high-accuracy automatic 2D to 3D file conversion for P&G researchers.
I will show you that by using convolutional coding with Viterbi decoding, you can achieve a BER of better than 1 x 10-7 at the same Eb/N0, 6 dB.
The DeepMind system used a deep convolutional neural network, with layers of tiled convolutional filters to mimic the effects of receptive fields.