Some Limitations Of Neural Networks

Some Limitations Of Neural Networks

Every algorithm that is used for solving a specific problem can have some limitations. These limitations do not even reduce its importance and the extent of usage. But the person who uses it regularly should have complete knowledge about it. Similarly, neural networks also have certain limitations in data science and machine learning. No one can deny the importance of neural networks in machine learning and data science. We know that neural networks are a series of algorithms with different variants and specifications. Some of the most prominent neural networks are RNNs and CNNs. But these models also have some limitations. We have listed some of the general limitations of artificial neural networks in machine learning and data sciences. learn more about Data Science Course in Pune

Training Time:

One of the biggest challenges for all neural network algorithms is the time taken by these algorithms while training the data set.The artificial neural network  these algorithms while training the data set. take much training time while solving simple real-world problems. But using some tricks, the efficiency of these algorithms can be increased very easily. The data scientists use some tricks to increase the efficiency of neural networks. The one way is to shuffle the training data set when the neural networks model takes so much time. Another way the data scientists use is to make some batches of the input data set before training the data. In this way, the training efficiency of the artificial neural network model is increased.

Theoretical Issues:

Many real-life problems have not been solved till now by using any of the artificial neural network models. Many other algorithms cannot solve some of the real-life problems. So this problem remains for many of the algorithms. Facebook could not have yet solved the issue of fake and misinformation and even the speech. Facebook users give reviews and feedback to solve this problem daily. Even artificial neural networks can effectively solve many problems, but some problems cannot be solved using artificial neural network algorithms. For more details visit Data Science Course in Chennai

Hardware Issues:

Many algorithms need to be run on the graphics processing units(GPUs) while using data science.  The artificial neural network algorithms like CNNs, while using complex data sets (images), cannot be run on the normal central processing units (CPUs). It becomes necessary to have graphics processing units to run artificial neural network algorithms like CNNs and RNNs. On the other hand, Google has introduced a google collab platform, which can be used to run machine learning and data science codes. This platform can even run the codes that can not be run without graphics processing units in the computer. In neural networks, the deep neural networks cannot be run without using the graphics neural network, but google collaboration can be used to run these algorithms as well. But it takes much time while running these algorithms. When the data set is in the form of images, these algorithms take too much to run on the google collab platform.

We have some of the major limitations of the neural networks and their types. Even there are specific limitations, but these algorithms are still the most powerful machine learning and data science algorithms. For more articles about data science, please keep visiting our blog.

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