Frequent question: What kinds of problems can neural networks approach?

Their strength lies in their ability to make sense out of complex, noisy, or nonlinear data. Neural networks can provide robust solutions to problems in a wide range of disciplines, particularly areas involving classification, prediction, filtering, optimization, pattern recognition, and function approximation.

What are the types of problems in which artificial neural network can be applied?

Definition of Neural Networks:

Researchers are designing artificial neural networks (ANNs) to solve a variety of problems in pattern recognition, prediction, optimization, associative memory, and control.

What is the biggest problem with neural networks?

The very most disadvantage of a neural network is its black box nature. Because it has the ability to approximate any function, study its structure but don’t give any insights on the structure of the function being approximated.

What can neural networks be used for?

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.

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What is the issue with neural network?

Another trouble which is encountered in neural networks, especially when they are deep is internal covariate shift. The statistical distribution of the input keeps changing as training proceeds. This can cause a significant change in the domain and hence, reduce training efficiency.

What are the disadvantages of neural networks?

Disadvantages of Artificial Neural Networks (ANN)

  • Hardware Dependence: …
  • Unexplained functioning of the network: …
  • Assurance of proper network structure: …
  • The difficulty of showing the problem to the network: …
  • The duration of the network is unknown:

How is hard learning problem solved neural network?

Challenging Optimization

Training deep learning neural networks is very challenging. The best general algorithm known for solving this problem is stochastic gradient descent, where model weights are updated each iteration using the backpropagation of error algorithm.

What are the advantages and disadvantages of using neural networks?

The network problem does not immediately corrode. Ability to train machine: Artificial neural networks learn events and make decisions by commenting on similar events. Parallel processing ability: Artificial neural networks have numerical strength that can perform more than one job at the same time.

What are the disadvantages in using a neural network to build a supervised model?


  • Neural networks are black boxes, meaning we cannot know how much each independent variable is influencing the dependent variables.
  • It is computationally very expensive and time consuming to train with traditional CPUs.
  • Neural networks depend a lot on training data.

What are the drawbacks of fuzzy logic and neural networks?

A major drawback of Fuzzy Logic control systems is that they are completely dependent on human knowledge and expertise. You have to regularly update the rules of a Fuzzy Logic control system. These systems cannot recognize machine learning or neural networks.

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How is neural networks different to social networks?

While a social network is made up of humans, a neural network is made up of neurons. Humans interact either with long reaching telecommunication devices or with their biologically given communication apparatus, while neurons grow dendrites and axons to receive and emit their messages.

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