Примери за използване на Deep neural на Английски и техните преводи на Български
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Deep neural networks are essentially a new way of computing.
Integration of the graph-based methods within the deep neural networks.
And yet deep neural networks somehow get the right answer.
Library for Deep  Learning based on deep neural networks.
Despite the huge success of deep neural networks, nobody is quite sure how they achieve their success.
Now that we have our audio in a format that's easy to process,we will feed it into a deep neural network.
Ho and her colleagues created a deep neural network to speed up the process.
So deep neural networks don't have to approximate any possible mathematical function, only a tiny subset of them.
Application of approaches with deep neural networks to the semantic knowledge.
To work around this, we have to use some special tricks andextra precessing in addition to a deep neural network.
One of these applications is the creation of deep neural networks- kind of like pared-down virtual brains.
Learn popular unsupervised learning methods, including clustering methodologies andsupervised methods such as deep neural networks.
And we have developed modern machine learning algorithms and deep neural networks to predict individual truck tours.
But deep neural networks bypass this barrier the same way as your own brain: learning to evaluate the course and choose the best.
The concept of deep  learning is sometimes just referred to as“deep neural networks,” referring to the many layers involved.
Modelling of the semantic information, encoded in the language resources,through the interaction between knowledge graphs and deep neural networks;
The former company are“training deep neural networks to understand music composition at a granular level”.
There is an area, dedicated to tensor calculus, used in applications of Artificial Intelligence such as,for example, a Deep Neural Network modeled in Tensorflow.
Not only will you learn how to use tools,such as deep neural networks, but you will gain a profound understanding of why they work.-.
But deep neural networks got around that barrier in the same way our own minds do, by learning to estimate what feels like the best move.
For that reason we will explore methods,based on deep neural networks, which are at the heart of all successful methods for Machine Learning.
Called deep neural networks, these complex mathematical systems allow machines to learn specific behavior by analyzing vast amounts of data.".
And using those two pieces of information, I can train a standard deep neural network or a deep  learning network to provide patient's diagnosis.
While you can train deep neural networks on one or more CPUs, the training tends to be slow, and by slow I'm not talking about seconds or minutes.
Last year, artificial intelligence(AI)research company DeepMind shared details on WaveNet, a deep neural network used to synthesize realistic human speech.
A deep  learning or deep neural network(DNN) framework covers a variety of neural  network topologies with many hidden layers.
The device combines a virtual-reality headset with Google's Deep  Dream algorithm,which uses a so-called deep neural network to analyze and enhance images.
In general, deep neural network computations run much faster on a GPU(specifically an Nvidia CUDA general-purpose GPU), TPU, or FPGA, rather than on a CPU.
The artist Adam Ferriss created this image using Google Deep  Dream,a program that adjusts an image to stimulate the pattern recognition capabilities of a deep neural network.
In general, deep neural network computations run an order of magnitude faster on a GPU(specifically an Nvidia CUDA general-purpose GPU, for most frameworks), rather than on a CPU.