Advantages of neural networks:
1. Non-linear function approximation: Neural networks are able to approximate any non-linear function, which makes them suitable for solving highly complex problems.
2. Adaptability: Neural networks have the ability to learn and adapt to new information, making them highly useful for applications that require real-time learning.
3. Parallel processing: Neural networks can process multiple inputs simultaneously, which can make them extremely fast and efficient.
4. Fault Tolerance: Neural networks are highly fault-tolerant, meaning that they can continue to function even if some of the nodes fail or become damaged.
5. Pattern Recognition: Neural networks are highly effective for image and speech recognition applications.
Disadvantages of Neural Networks:
1. Lack of transparency: Neural networks are often described as black boxes because it is difficult to understand how they arrive at their outputs.
2. Training complexity: Training a neural network can be complex and time-consuming, often requiring large datasets and a considerable amount of computational resources.
3. Overfitting: Neural networks can be prone to overfitting, meaning that they may perform well on the training data but poorly on new data.
4. High computational resources: Neural networks require significant computational resources (e.g., GPUs) to train and run, making them expensive to maintain.
5. Limited data support: Neural networks are not suited for applications with limited data, as they require large datasets and high-quality training data to perform effectively.
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