Preložiť "neurali" do Angličtina

EN Jump to ... What are neural networks? How do neural networks work? Types of neural networks Neural networks vs. deep learning History of neural networks Neural networks and IBM Cloud menu icon

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EN Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms

• Neural networks
• known as artificial
• networks
• ANNs
• simulated neural networks
• SNNs
• subset of machine
• heart of deep
• algorithms

EN Neural networks, or artificial neural networks (ANNs), are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer

• Neural networks
• artificial neural networks
• ANNs
• layers
• containing an input
• hidden layers
• output layer

EN Neural networks expand human possibility, overcome language barriers,and bring cultures closer together.

• Neural networks expand
• possibility
• overcome language
• bring cultures closer

EN DeepL’s neural networks are able to capture even the slightest nuances and reproduce them in translation unlike any other service

• nuances

EN The exceptional quality of DeepL's machine translation services is based on proprietary improvements that the team has achieved in neural network mathematics and methodology.

• based on proprietary
• team has achieved
• neural network mathematics

EN For the first time, the newly-designed neural networks were able to translate even longer passages without errors

• newly-designed neural networks
• able to translate
• longer passages without

EN The neural networks are training to master further languages, including Mandarin and Japanese.

• master further languages
• Mandarin

EN DeepL’s networks consistently outperform other translation systems, making ours the world’s best translation machine

• networks consistently outperform
• translation systems
• translation machine

EN DeepL achieves further research breakthroughs in the architecture of neural networks; another quantum leap in translation quality

• DeepL achieves further
• neural networks
• another quantum leap
• translation quality

EN Within Linguee GmbH, a team led by Jaroslaw Kutylowski begins working on DeepL Translator, a novel online translator based on neural networks.

• Within Linguee GmbH
• Kutylowski begins working
• DeepL Translator
• novel online translator
• neural

EN Easily investigate data using a wide variety of traditional and modern statistical models, from decision trees to regression models to neural networks.

• modern statistical models
• models to neural

EN The neural networks in HTR+ learn quickly and the more training data they have, the better the results will be.

• learn quickly
• better the results

EN Easily investigate data using a wide variety of traditional and modern statistical models, from decision trees to regression models to neural networks.

• modern statistical models
• models to neural

EN Deep Learning is therefore part of the family of active principles called Neural Networks, which are in turn part of the family of Machine Learning?s active principles, the whole all incorporated into the generic medicine, Artificial Intelligence.

• active principles called
• Networks
• family of Machine
• active principles
• whole all incorporated
• generic medicine
• Artificial

EN Deep Learning Inference Cameras

• Deep Learning Inference

EN Knowledge of Neural Networks, Machine Learning, Deep learning

• Knowledge of Neural
• Machine Learning
• Deep learning

EN Roadside Assistance Website Template

• Roadside Assistance Website

EN This is because physical activity increases brain oxygenation and blood circulation, triggering the neural circuits linked to attention, focus and creativity.

• because physical activity
• brain oxygenation
• blood circulation
• triggering the neural
• linked to attention
• focus

EN The areas of application for AI have greatly expanded, and the current focus is on real-time and predictive applications and deep learning (based on artificial neural networks with many intermediate layers).

• areas of application
• AI have greatly
• real-time
• predictive applications
• deep learning
• based on artificial
• layers

EN In fact, I worked in the Neural Networks Research Centre of Helsinki University of Technology in the 90s.

• fact
• Networks Research Centre

EN In paid versions we use Google neural translations which are very accurate when translating to and from English language.

• Google neural translations
• English

EN Human translators generally rank at 8.5 and new neural machine translations hit 8.3.

• Human translators generally
• neural machine translations

EN Deep learning neural networks are ideally suited to take advantage of multiple processors, distributing workloads seamlessly and efficiently across different processor types and quantities

• Deep learning neural
• advantage of multiple
• distributing workloads seamlessly
• efficiently across different
• types
• quantities

EN Easily investigate data using a wide variety of traditional and modern statistical models, from decision trees to regression models to neural networks.

• modern statistical models
• models to neural

EN Amazon CodeGuru Reviewer is trained using rule mining and supervised machine learning models that use a combination of logistic regression and neural networks.

• Amazon CodeGuru Reviewer
• trained using rule
• supervised machine learning
• combination of logistic
• neural

EN The Iris Core AI Analytics app combines multiple features based on deep neural networks in one application

• Iris Core AI
• app combines multiple
• based on deep

EN AI-FaceDetect-Deep is based on advanced artificial vision and artificial intelligence algorithms and uses deep neural networks, both for face detection and for face classification.

• AI-FaceDetect-Deep is based
• advanced artificial vision
• artificial intelligence algorithms
• uses deep neural
• both for face
• face

EN From the phones in our pockets to the reality of self-driving cars, the consumer economy has started to tap into the power of deep learning?s neural networks

• reality of self-driving
• deep learning
• neural networks

EN Blockchains can therefore represent extraordinary knowledge bases which, fed to specific neural networks, can be used profitably by dedicated Artificial Intelligence algorithms.

• Blockchains can therefore
• extraordinary knowledge bases
• fed to specific
• networks
• Artificial Intelligence

EN Likewise, many Artificial Intelligence algorithms are, under some conditions, hungering for performance due to the complexity of neural networks

• Likewise
• Artificial Intelligence algorithms
• under some conditions
• hungering for performance
• complexity of neural

EN Finally, there are hypotheses of integration, between Blockchain and Artificial Intelligence, able to mitigate black-box phenomena and the introduction of bias within neural networks

• Finally
• hypotheses of integration
• between Blockchain
• Artificial Intelligence
• able to mitigate
• phenomena
• introduction of bias
• neural networks

EN Deep Learning is therefore part of the family of active principles called Neural Networks, which are in turn part of the family of Machine Learning?s active principles, the whole all incorporated into the generic medicine, Artificial Intelligence.

• active principles called
• Networks
• family of Machine
• active principles
• whole all incorporated
• generic medicine
• Artificial

EN Neural networks are designed to work in a manner that is similar to the functioning of the brain

EN Just like stimuli causes the firing of neurons in the brain that enable action, neural networks use inputs with a threshold requirement

• Just like stimuli
• firing of neurons
• brain that enable
• threshold requirement

EN Deep learning is actually comprised of neural networks

• comprised of neural

EN 1980s: Neural networks which use a backpropagation algorithm to train itself become widely used in AI applications.

• itself become widely
• AI

EN Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence

• Machine learning
• deep learning
• sub-fields of artificial

EN However, deep learning is actually a sub-field of machine learning, and neural networks is a sub-field of deep learning.

• However
• sub-field of machine
• sub-field of deep

EN Deep learning and neural networks are primarily credited with accelerating progress in areas, such as computer vision, natural language processing, and speech recognition.

• Deep learning
• credited with accelerating
• areas
• computer vision
• natural language processing
• speech

EN See the blog post “AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the Difference?” for a closer look at how the different concepts relate.

• blog post
• AI vs
• Machine Learning vs
• Deep Learning vs
• Neural Networks
• Difference
• different concepts

EN Some methods used in supervised learning include neural networks, naïve bayes, linear regression, logistic regression, random forest, support vector machine (SVM), and more.

• learning include neural
• naïve bayes
• linear regression
• logistic regression
• random forest
• vector machine
• SVM

EN Other algorithms used in unsupervised learning include neural networks, k-means clustering, probabilistic clustering methods, and more.

• learning include neural
• k-means clustering
• probabilistic clustering methods

EN What are Neural Networks? - United Kingdom | IBM

• Neural Networks
• United KingdomxIBM

EN Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning.

• Neural networks reflect
• brain
• allowing computer programs
• recognize patterns
• solve common problems
• fields of AI
• machine learning
• deep

EN Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer

• Artificial neural networks
• ANNs
• layers
• containing an input
• hidden layers
• output layer

EN Neural networks rely on training data to learn and improve their accuracy over time

• Neural networks rely
• improve their accuracy

EN One of the most well-known neural networks is Google’s search algorithm.

• well-known neural networks
• search

EN In the example above, we used perceptrons to illustrate some of the mathematics at play here, but neural networks leverage sigmoid neurons, which are distinguished by having values between 0 and 1

• example above
• mathematics at play
• neural networks leverage
• neurons
• distinguished by having
• between

EN As we start to think about more practical use cases for neural networks, like image recognition or classification, we’ll leverage supervised learning, or labeled datasets, to train the algorithm

• start to think
• practical use cases
• neural networks
• image recognition
• classification
• leverage supervised learning
• labeled datasets
• train the algorithm