Examples of using Artificial neural in English and their translations into Indonesian
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The story is about an ANN, or artificial neural network.
By experience, I can tell that artificial neural networks provide real solutions that are difficult to match with other technologies.
Now, does anyone remember the day Google fired up the artificial neural net known as RankBrain?
However, running an artificial neural network consumes a lot of time and energy.
To achieve this,deep learning uses a layered structure of algorithms called an artificial neural network(ANN).
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The modern usage of the term often refers to artificial neural networks, which are composed of artificial  neurons or nodes.
To achieve this,deep learning uses a layered structure of algorithms called an artificial neural network(ANN).
The recipe below was generated by an artificial neural network, a type of artificial  intelligence(AI) that learns by example.
Now we come to the discussion of deep learning,which can be interpreted as a series of methods for training multi-layer artificial neural networks.
Speech recognition using linear predictive coding and artificial neural network for controlling movement of mobile robot.
Artificial neural networks- Non-linear predictive models that learn through training and resemble biological neural  networks in structure.
Information Technology and Cybersecurity firms have begun to adopt artificial neural networks in order to monitor and prevent DDoS attacks.
Artificial Neural Networks: Non-linear predictive models that learn through training and resemble biological neural  networks in structure.
While scientists have had remarkable success with artificial neural networks, contemporary AI systems have been stymied by a specific limitation.
Artificial neural networks were designed to model some properties of biological neural  networks, though most of the applications are of technical nature as opposed to cognitive models.
Mimicking the processes found in biological neurons, artificial neural networks are used to learn and predict based on a given data set.
The backpropagation algorithm, in combination with a supervised error-correction learning rule,is one of the most popular and robust tools in the training of artificial neural networks.
Systems like IBM's Watson and Google's Alpha equip artificial neural networks with enormous computing power, and accomplish impressive feats.
New artificial neural networks developed by the Max Planck Institute of Neurobiology and Google AI can now even recognize and classify nerve cells independently based on their appearance.
Deep Learning is a subset of machine learning, where artificial neural networks, algorithms inspired by human brains, from large quantities data.
With the rise of the Web of Things,Data Know-how and Cybersecurity firms have begun to adopt artificial neural networks in order to monitor and stop DDoS attacks.
Google and Facebook are building large Artificial Neural Networks(ANN) that can instantly recognize faces, cars, buildings, and other objects in digital photos.
With the rise of the Internet of Things, Information Technology andCyber Security firms have begun to adopt artificial neural networks in order to monitor and prevent DDoS attacks.
Most of the currently employed artificial neural networks for artificial  intelligence are based on statistical estimation, optimization and control theory.
Where the nervous system uses biological synapses to process chemical andelectrical signals, artificial neural networks use synthetic synapses to process digital information.
Deep learning is a division of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data.
With the rise of the Internet of Things,Information Expertise and Cybersecurity companies have begun to undertake artificial neural networks with a purpose to monitor and prevent DDoS assaults.
The deep learningis the subset of Machine learning where artificial neural network, algorithms inspired by the human brain, learns from a large amount of data.
However, the machine learning algorithm thatplays a role in life in the world is artificial neural networks, a technique inspired by the workings of neurons in the human brain.
Robat delineates the borders of objects it encounters and classifies them using an artificial neural network, thus creating a rich, accurate map of its environment while avoiding obstacles.