What are the pros and cons of using neural networks?

What are 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:

What is neural network and its advantages?

Advantages of Neural Networks:

Neural Networks have the ability to learn by themselves and produce the output that is not limited to the input provided to them. The input is stored in its own networks instead of a database, hence the loss of data does not affect its working.

What are pros of neural networks over computers?

What are the advantages of neural networks over conventional computers? Explanation: Neural networks learn by example. They are more fault tolerant because they are always able to respond and small changes in input do not normally cause a change in output.

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.

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What are the advantages of neural networks ability to learn by example?

Explanation: Neural networks learn by example. They are more fault tolerant because they are always able to respond and small changes in input do not normally cause a change in output. Because of their parallel architecture, high computational rates are achieved.

What is the disadvantage of deep learning?

Main disadvantages: It requires very large amount of data in order to perform better than other techniques. It is extremely expensive to train due to complex data models.

What is the importance of neural network?

Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning.

Why is neural network better than decision tree?

Neural networks are often compared to decision trees because both methods can model data that has nonlinear relationships between variables, and both can handle interactions between variables. … A neural network is more of a “black box” that delivers results without an explanation of how the results were derived.

What are the appropriate problems for neural network learning?

Appropriate Problems for ANN

  • training data is noisy, complex sensor data.
  • also problems where symbolic algos are used (decision tree learning (DTL)) – ANN and DTL produce results of comparable accuracy.
  • instances are attribute-value pairs, attributes may be highly correlated or independent, values can be any real value.

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.

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