Examples of 'neural networks' in a sentence
Meaning of "neural networks"
Neural networks are a type of artificial intelligence (AI) models designed to simulate the functioning of the human brain to perform tasks such as pattern recognition, data analysis, and machine learning. They are composed of interconnected artificial neurons that process and transmit information, enabling complex computations and learning from data
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- plural of neural network
How to use "neural networks" in a sentence
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neural networks
I looked at the neural networks operating system.
Neural networks are very effective in identifying patterns.
Some information about neural networks in general.
Neural networks are not limited to image recognition.
Use GPUs to train neural networks faster.
Using neural networks for survival prediction.
Lightning can actually modify neural networks.
Artificial neural networks extract facial expression.
Evolutionary reinforcement learning of artificial neural networks.
Artificial neural networks have many variations.
Also methods exist based on neural networks.
Train neural networks without prior knowledge.
Genetic reinforcement learning for neural networks.
Neural networks is not a completely new idea.
In this sense neural networks learn a mapping.
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Neural networks are going to add another layer of complexity.
Unfully interconnected neural networks as associative memory.
Neural networks in digital exchange alarm classification.
These models are based on biological neural networks.
Most neural networks are not very big.
This helps create new neural networks.
Neural networks networks later in this course.
A form of regularization useful in training neural networks.
Deep neural networks are a truly disruptive technology.
It provides easy mechanisms to express neural networks.
Artificial neural networks struggle to learn similarly.
Cognitive skills are supported by certain neural networks.
We train neural networks and reproduce instinct.
All this is made possible by neural networks.
Deep neural networks operate in two stages.
Classification and clustering with neural networks.
Npv layered neural networks are then produced.
Complex relational reasoning with neural networks.
Artificial neural networks are flexible and adaptive.
Go one step deeper with neural networks.
Neural networks are made up of a plurality of associated neurones.
Animation of human gait using neural networks.
Neural networks as for video signal processing.
Interpretation of process analysis based on neural networks.
Neural networks learn from existing data.
These models are analyzed by the neural networks.
Training of neural networks is a well known procedure.
Its nonlinearity is approximated by artificial neural networks.
How neural networks learn to recognize patterns.
The second problem involves neural networks.
Recurrent neural networks for sequenced data.
The second one is inspired by neural networks.
Well neural networks can act as classifiers.
We will get deep learning in neural networks.
Neural networks generally operate in two phases.
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Networks for potential collaboration and partnership
Building on existing networks in the health sector
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Examples of using Neural
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Now the neural circuits will connect
The arachnid seeks to harvest neural energy
The neural cortex looks like icing
