One of the popular forms of research being conducted worldwide is about neural networks. Lately, the research has gained more momentum. A few years back, only a few things were explored about this study because of computational and data limitations. But, now researchers have come up with great results.
As you go through the article, you will find out about these networks and the simple ways to boost their performance.
Let’s introduce you to Neural Networks.
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What should you know about neural networks?
A neural network is a subfield of machine learning in artificial intelligence. It is a sequence of algorithms that work to identify hidden connections in a set of data via a technique that mimics the human brain. You can understand it as a system of neurons, which can be artificial or organic.
They are created to work just like the human brain works. It is a modern approach to artificial intelligence and is also called deep learning. The idea of such networks has been around for 70 years. The technique was first proposed by Warren McCullough and Walter Pitts in 1944.
The goal of them was to build a system that could solve problems as a human brain does. However, later the attention shifted to using neural networks for diverse tasks such as social network filtering, medical assessment, and speech recognition.
How do neural networks work?
Nets include several layers, which are made of nodes. A node is patterned in the neuron of the human brain. It is an area where computation occurs and it is activated when it receives enough stimuli. The group of neurons receives the input from other nodes and creates output without programmed rules. They find solutions to issues through trial and error.
They imitate the behavior of the human brain and let computer programs make better predictions on unseen data. It is used to identify configurations and decode issues of deep learning, artificial intelligence, and machine learning.
Application of neural networks in several sectors
Neural networks enhance deep learning systems and are applied to various business sectors and industries such as financial services, assessment of risks, marketing research, and forecasting.
- Public sector: Public sectors make use of them for facial recognition, create smart cities models, and build artificial security intelligence
- Health sectors: They are used in health sectors for predictive diagnosis, health checkups, and biomedical imaging.
- Banking: These techniques are applied in banks to identify frauds, automate financial adviser facilities and conduct credit analysis.
- Manufacturing: Manufacturing sectors make use of such networks to improve supply chains, estimate the requirement of energy, and find defects.
- Retail: The retail industries apply these nets processes for improving customer intelligence and conducting network analysis.
- Gaming industries: They work to communicate with dynamic environments and solve complicated issues just like in real life. You can train your nets to play card games and video games.
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5 tips to boost neural networks performance
Let’s find out some simple techniques to boost the performance of nets. These strategies will help in improving both its speed and accuracy.
1. Increase hidden layers:
The researchers worked on a single hidden layer and got great results. They have always wondered about the effect of adding more hidden layers, and later it has been proved that adding more layers helped in getting better and superior results.
More layers can be generated with the help of mlp function and neural net. When adding layers, you must test with different numbers of layers. Begin with increasing a single layer and then slowly increasing it if you don’t find any improvement in the results.
2. Make changes to the activation function:
You will get accurate outputs by making changes to the activation function. Researchers have worked with tanh, sigmoid, and rectified linear units. They, however, achieved positive results with the rectified linear unit.
3. Make changes to the activation function in the output layer:
Changing the activation function in the output layer instead of hidden layers sometimes gives better results. So, it is suggested to use varying activation functions in output neurons.
4. Increasing neurons:
Nets will not work on complex data if the numbers of used neurons are not sufficient. In such cases, the results would be weak. Conversely, if the numbers of neurons are more than required, the networks will take a lot of time to give results and it may not be accurate. This is called overfitting and it creates random noise in the data. So, the tip is to choose the perfect number of neurons.
5. Use more data:
They work best in the presence of a lot of data. The lack of data usually results in overfitting. It is a common issue that happens when using these networks so you must use the data that covers the problem data space.
The positive impact of neural networks on gaming!
Neural networks have been integrated into every digital device of our life. Popular virtual assistants such as Alexa, Siri, and Google Assistant are based on deep learning. This newly found technique has entered into several sectors such as Oil and Gas, healthcare, and Financial industries.
These networks have made computer systems more efficient by making them think like humans. Even gaming industries make use of them in a variety of ways such as controlling a game agent or any other element of a computer game. It can implement numerous new features and change the future of gaming.
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