We knew that there are specific limitations of each artificial neural network algorithms. Some artificial neural network algorithms like deep neural networks cannot be run on regular computers and need to be run on graphics processing units. The normal central processing units cannot manage these complex deep neural networks. On the other hand, Google tried to solve this problem by introducing a cloud-based graphics processing unit. But still, this problem arises even using the google collab platform. Moreover, the neural network algorithms take much training time, which cannot be tolerated in many cases where the scientists need to have efficient and fast learning procedures. The scientists are trying to make some changes in the algorithms to make them efficient.
Moreover, the artificial neural network algorithms and, more specifically, the deep neural networks cannot solve all daily life problems. In many cases, the algorithms fail. The scientists are going to update the neural networks in such a manner that they can be enabled to solve all the problems. But updating them in such a way will take a lot of time and research. The scientists want to solve all the problems related to natural language processing, emotion engines, and common sense engines. Our team of experts has discussed some of the future developments and updations in neural networks and deep neural networks. Know more about Data Science Course in Pune
Future Developments in Artificial Neural Networks:
Here are some of the developments that the data scientists and researchers are trying their best to make in the future.
Fuzzy Logic Integrations:
The fuzzy logic works on true and false values. The data scientists and researchers are working to integrate the fuzzy logic into neural network algorithms, specifically deep neural networks. These algorithms will be the most powerful systems of the current era. Some applications of these algorithms will be screening the job applications, auto engineering, automatic controls of the crane, etc.
Building The Specialized Hardware:
The scientists and researchers are working to build specialized hardware integrated with the neural network and all of the neural networks’ variants, specifically the deep neural networks. Suppose the scientists succeed in building such hardware integrated with neural network algorithms. In that case, it will become much easier to learn the neural networks and use them to solve every daily life problem. But it will be much costly hardware. Some of the United States of America scientists stated that they would provide the cost-effective hardware integrated with the neural network algorithms. In the future, the problem solving with neural networks will become easier.
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We know that artificial neural networks mimic the human brain, but these algorithms’ accuracy is not as effective as the human brain. But still, artificial neural networks are the most powerful models that can deal with daily life problems with more accuracy. Scientists are working to improve the accuracy of neural network algorithms and deep neural networks. In the coming years, they will be able to increase the neural networks’ efficiency and accuracy. We have discussed some of the future improvements in artificial neural networks. For more articles related to data science, please keep visiting our blog. Learn more about Data Science Course in Chennai
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