Examples of using Deep learning techniques in English and their translations into Vietnamese
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Recently, reinforcement learning(RL)has been gaining popularity compared to other deep learning techniques.
Insilico Medicine aims to dramatically shorten this time by applying deep learning techniques.
In this paper, the use of deep learning techniques to hide secret audio into the digital images is proposed.
First author Marc Huertas-Company, an astronomer at the Paris Observatory and Paris Diderot University,had already conducted pioneering work applying deep learning techniques to galaxy classifications using freely available CANDELS data.
The other important point with deep learning techniques is that, for the most part, the solution of what can be done is defined by the'input data space'.
In November 2016, Google has transitioned its translating method to a system called“Neural Machine Translation.”[5]It uses Deep Learning techniques to translate the whole sentences at a time and ensures greater accuracy of the context.[3].
Deep Learning techniques can be used for both retrieval-based or generative models, but research seems to be moving into the generative direction.
The main use of its technologywas a visual search engine that applied deep learning techniques using artificial neural networks for the fashion industry.
Deep learning techniques have also helped us to find solutions to previously impossible problems, and this will continue to be a trend well into the future.
DeepCoder is a project run by Microsoft and the University of Cambridge,using deep learning techniques to mimic the neural network of a brain, where vast amounts of data are processed and evaluated to make decisions.
Deep Learning techniques have been successfully applied in Image Processing tasks, and a straightforward example for its use is in Edge Detection.
Facebook explains that manipulations can be made through simple technology like Photoshop orthrough sophisticated tools that use artificial intelligence or“deep learning” techniques to create videos that distort reality- usually called“deepfakes.”.
I originally wanted to do some cool deep learning techniques, but I realized that took massive datasets and more time than I wanted to spend.
Deep learning techniques are data-hungry, meaning that AI algorithms built on deep learning can only work accurately when they're trained and validated on massive amounts of data.
It has been shown that simple deep learning techniques like CNN can, in some cases, imitate the knowledge of experts in medicine and other fields.
Deep learning techniques will be used to train 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.
The AI lab will develop deep learning techniques to help Facebook do tasks such as automatically tagging uploaded pictures with the names of the people in them.
New deep learning techniques have enabled companies like NTT East and Earth Eyes to analyze video footage more quickly and cheaply than ever before, and a larger number of other companies in Japan, America, and China are developing products with similar capabilities.
In this series I want to go over some of the Deep Learning techniques that are used to build conversational agents, starting off by explaining where we are right now, what's possible, and what will stay nearly impossible for at least a little while.
The neural networks, the core aspect of deep learning techniques for developing self-improving software needs a large amount of data to be trained on, Apple is necessarily at a disadvantage because it only has access to publicly available sets.
Yet because neural networks- the backbone of deep learning techniques for developing self-improving software- require large amounts of data to be trained on, Apple is necessarily at a disadvantage because it only has access to publicly available sets.
Based on visual data and the deep learning technique, the robot could practice in a digital crop.
For example, if you give a computer a picture of a deep learning technique, each layer in this artificial neural network will uniquely see the problem.
By utilizing deep learning processing techniques, such as convolutional neural network(CNN) algorithms, for temporal satellite imagery analysis, timely results are obtained and provided to the users.
Multiple National Engineering Laboratories have also been established,working on both state-of-the-art paradigms, like deep learning, and not yet feasible techniques for constructing machine intelligence.
Deep learning and neural networks-- advanced techniques of machine learning- perform complex computations that demand a combination of CPUs and GPUs.
Through the use of traditional machine learning techniques, and more recently with advancements in Deep Learning, there is noticeable progress being made in computers being able to interpret and react to what they“see”.
Deep learning is a powerful statistical technique for classifying patterns using large training data sets and multi-layer AI neural networks.