Examples of using Convolutional in English and their translations into Portuguese
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The definition of a convolutional neural network CNN.
What convolutional neural networks are and how they are built.
Several algorithms exist for decoding convolutional codes.
AU On the optimization of convolutional neural networks using BAT algorithm.
Convolutional codes, by contrast, continuously add redundant bits and have an arbitrary length.
I already mentioned neural networks, but convolutional neural networks are also a needed(and planned) addition.
Deeplearning4j: An open-source deep learning library written for Java/C++ w/LSTMs and convolutional networks.
Decoding convolutional codes==Several algorithms exist for decoding convolutional codes.
In this work, we propose a robust deep learning convolutional neural network(cnn) model named deep nlp.
Learn about convolutional neural networks(CNN) and compare them to the fully connected neural networks already introduced.
Learn the basics of deep learning, the fundamentals of neural, convolutional, and recurrent network architectures, and more.
A wide variety of ECCs have been developed, but they generally can be classified into two main types:block and convolutional.
It implements a series of convolutional neural networks(CNNs), optimized for the web and for mobile devices.
Take advantage of the large memory capacity in CPUs to effectively support 3D convolutional neural networks(CNNs) for medical image analysis.
On the optimization of convolutional neural networks and its application for Face-based emotion recognition.
To address these problems,in this work we propose a facial expression recognition system that uses convolutional neural networks.
Four pattern reconigition techniques were tested: convolutional neural networks, hog+svm, haar cascade and lbp cascade.
Based on convolutional neural networks(CNN), this toolkit extends workloads across Intel® hardware and maximizes performance.
From latent factors obtained by matrix factorization a convolutional neural network is trained to learn these factors using the audio.
Based on convolutional neural networks(CNN), the toolkit extends workloads across Intel® hardware and maximizes performance. Intel® AI DevCloud.
Current object detection models are built on convolutional neural networks(CNN), a specific architecture of neural networks.
Classic convolutional neural networks(CNNs) and architectures such as AlexNet, VGGNet, GoogleNet, and ResNet, are widely used as the baseline model in facial recognition.
Data augmentation and di erent preprocessing steps were studied together with various convolutional neural networks architectures.
Implementation of a convolutional layer using approximate multipliers in FPGA for convolutional neural networks, BE.EP. IC.
In this work, the dislocation density was analyzed through peak broadening of x-ray diffraction(xrd)using convolutional multiple whole profile(cmwp) program.
The convolutional neural networks, pre-trained by EOS Data Analytics to extract features from imagery, let you apply state-of-art methods to detect objects and track changes from space.
Nevertheless, pnns presented promising results when compared to other state-of-the-art algorithms for the same purposes, such as convolutional neural network(cnn) and support vector machine svm.
Overview of Convolutional Neural Networks for Image Classification Read a comprehensive introduction to convolutional neural networks(CNN) and how they are useful for image classification.
Another option, turbo code, is a block code built from two ormore relatively simple convolutional codes plus interleaving that creates a more uniform distribution of errors.
Learn about a new method for convolutional layers for x86 architectures(such as Intel® Xeon PhiTM and Intel® Xeon® processors) that provides high-efficiency layer operations on multinode systems.