A neural network is a collection of algorithms that seek to identify underlying relationships in a dataset through a mechanism that imitates the way the human brain works. The neural networks may respond to evolving inputs, therefore the network produces the best possible output without redesigning the performance criteria. The neural network characteristics are a computer system with interconnected nodes that function like neurons in a human brain. They use neural networks to detect similarities and hidden trends in raw data as well as cluster and identify raw data and to learn and improve continuously over time. Let us check out the advantages and disadvantages of neural networks to know more about the concept.
Pros OR Advantages of neural networks:
- Neural networks have the ability to learn on their own and generate output that is not limited to the input they provide.
- The input data is stored in its own networks instead of the database. Hence, data loss does not affect the way it operates.
- The neural network will learn from instances and adapt them when a similar event occurs, thereby allowing them to function through an event in real-time.
- Even if the neuron does not respond or information is lost, the network is still able to detect the fault and generate the output.
- Neural networks conduct multiple tasks in parallel without impacting the performance of the system.
- Storing information on the entire network.
- Ability to work with incomplete knowledge.
- Having fault tolerance.
- Having a distributed memory.
- Gradual corruption.
- Ability to make machine learning.
- Parallel processing capability.
Cons OR Disadvantages of neural network:
- The main disadvantages of neural networks are their black-box nature.
- Sometimes you need more control over the details of the algorithm, although there are libraries like Keras that make the development of neural networks fairly simple.
- Neural networks usually require much more data than traditional algorithms, as in at least thousands if not millions of labelled samples.
- Neural networks are also more complex in computing terms than traditional algorithms.
- The duration of the neural network is unknown.
- Hardware dependence.
- Unexplained behaviour of the network.
- Determination of proper network structure.
- The difficulty of showing the problem of the network.
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