Advantages and Disadvantages of Artificial Neural Network
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We have collected some solid points that will help you understand the pros and cons of Artificial Neural Network in detail.
But first, let’s understand the topic:
What is Artificial Neural Network?
An artificial neural network is a type of neural network that is designed to simulate the structure and function of the human brain. It is used in a wide range of applications, from finance to image processing.
What are the advantages and disadvantages of Artificial Neural Network
The followings are the advantages and disadvantages of Artificial Neural Network:
|Non-Linear Relationships||Limited Interpretability|
|Fault Tolerance||Computationally Expensive|
|Parallel Processing||Data Requirements|
|Generalization Ability||Sensitivity to Noise|
Advantages of Artificial Neural Network
- Learning Ability – One of the main advantages of ANNs is their ability to learn and adapt to new situations. They can be trained on large datasets and learn patterns that are not easily discernible by humans.
- Non-Linear Relationships – ANNs are capable of learning non-linear relationships between inputs and outputs, making them useful in a wide range of applications such as image and speech recognition.
- Fault Tolerance – ANNs are also able to tolerate faults, meaning that they can still function correctly even if some of the neurons in the network are damaged or destroyed.
- Parallel Processing – Another advantage of ANNs is their ability to perform many calculations simultaneously, which allows them to process large amounts of data quickly and efficiently.
- Generalization Ability – ANNs can generalize from examples they have seen during training and apply their learning to new data. This means that they can make accurate predictions even on data they have not seen before.
Disadvantages of Artificial Neural Network
- Overfitting – ANNs can sometimes become too specialized and only able to work with a specific type of data. This can lead to overfitting, where the network becomes so focused on the training data that it’s unable to generalize to new data.
- Limited Interpretability – Unlike traditional statistical models, ANNs are often considered “black boxes,” meaning we don’t always understand how they arrived at their conclusions. This can be a disadvantage in situations where we need to know how the network made a particular decision.
- Computationally Expensive – Training an ANN can require a lot of computational power and time, especially for large datasets. This can be a disadvantage for organizations that don’t have access to powerful computing resources.
- Data Requirements – ANNs require a large amount of data to learn effectively. If the dataset is small or biased, the network may not be able to learn the underlying patterns and relationships between the data points.
- Sensitivity to Noise – ANNs can be sensitive to noise in the data, which can lead to inaccurate predictions or classifications. It’s important to preprocess the data to reduce noise before feeding it to the network.
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