What are the learning strategies for artificial neural networks?

What are the learning strategies for artificial neural networks?

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Machine Learning in ANNs

  • Supervised Learning − It involves a teacher that is scholar than the ANN itself.
  • Unsupervised Learning − This method becomes required when there is no example data set with known answers.
  • Reinforcement Learning − Moreover, a strategy built on observation.
What are the main component of artificial neural networks?
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A simple neural network consists of three components:
  • Input layer.
  • Hidden layer.
  • Output layer.
What are artificial neural networks used for?
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Artificial Neural Network (ANN) will use the processing of the brain as a basis to develop algorithms. Furthermore, algorithms that can model complex patterns and prediction problems from aviation and society to complex mathematics.

However, what many people seem to be missing about artificial neural networks is that there can be sudden capability gains.

They are not just getting predictably better with more training, data, and parameters. But, sometimes capabilities emerge in a jumpy and unpredictable way.

Moreover, there can be sudden phase transitions. As a result, this makes it even harder to predict when transformative AI might appear.

References:- https://arxiv.org/abs/2202.07785https://ar5iv.labs.arxiv.org/html/2201.11903https://arxiv.org/abs/2204.02311
Stanford Professor & Nobel Prize Winner Paul Romer

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What are the learning strategies for artificial neural networks?