Why do we need complex valued neural networks?

The Hopfield and cellular neural networks are also presented. Complexvalued neural networks, their naturalness and necessity are observed in Section 1.4. It is shown that a single complex-valued neuron can learn non-linearly separable input/output mappings and is much more functional than a single real-valued neuron.

What type of applications are possible thanks to neural networks?

Neural networks are a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data. They are used in a variety of applications in financial services, from forecasting and marketing research to fraud detection and risk assessment.

What is neural network theory?

Neural networks aim to mimic the human brain — and one way to think about the brain is that it works by accreting smaller abstractions into larger ones. Neural networks have to work for it. As with the brain, neural networks are made of building blocks called “neurons” that are connected in various ways.

What is complex valued neural networks?

Complex-valued neural networks (CVNN) are ANNs that process information using complex-valued parameters and variables [4]. The main reason for their advocacy lies in the difference between the representation of the arithmetic of complex numbers, especially the multiplication operation.

Does machine learning use complex numbers?

Using complex numbers in machine learning algorithm layers can help smooth rugged data valleys like this one into smoother, locally flatter surfaces with multiple tiers that allow algorithms to identify more objects with more precision.

Which are common applications of deep learning in AI?

Common Deep Learning Applications

• Fraud detection.
• Customer relationship management systems.
• Computer vision.
• Vocal AI.
• Natural language processing.
• Data refining.
• Autonomous vehicles.
• Supercomputers.