Deep learning and Neural Networks: Can you consider them to be the Right Choice?
Since people tend to use neural networks everywhere nowadays, do you believe that they are always the best option?
Right now, deep learning is undoubtedly one of the hottest tech subjects. This sophisticated technology is leveraged by big companies and young start-ups alike. If you think big data is important, then deep learning should be essential to you. Since people tend to use neural networks everywhere nowadays, do you believe that they are always the best option?
Buzz Around Deep Learning
For emerging technology, buzz can be a very powerful thing, as there are chances that they will struggle to meet planned promises or commitments that are inflated beyond reality. For example machine learning. It vanished in 2018, without even making it to the productivity threshold.
Since AI has been the talk of the nation for quite a while, deep learning has also generated significant interest. While in the past couple of years it has done enormous things, there are individuals who still believe that the talk about deep learning will eventually go out of trend as they understand it has very serious limitations as it is nothing more than a heap of codes and stats. They assume it is a statistical equation that through statistics and coding, it finds a pattern in knowledge.
Even though deep learning integrates neural networks into their design, the deep learning and neural networks make a strong distinction:
Although neurons are used by Neural Networks to transmit data via connections in the form of input data and output values, Deep Learning is concerned with the conversion and extraction of features that seek to make a connection between stimuli and concerned brain neural responses.
Neural Networks Disadvantages:
Neural networks need much more knowledge than any other conventional algorithms for machine learning. This is a huge issue, and with less data in any other algorithms, many machine learning problems can be fixed. This adds to the issue of over-fitting and generalization. The method depends more on the data for training and can be tailored to the data. While there are several situations where little data is dealt with by the neural network, most of the time they don’t.
The complexity of presenting the issue to the network
With numerical details, ANNs can operate. Before being added to ANN, issues have to be converted into numerical values. The display process to be evaluated will affect the network’s output directly. This is based on the capability of the user.
Proper network structure determination
For determining the structure of artificial neural networks, there is no clear law. Experience and trial and error are used to achieve the required network structure.
In general, neural networks are also more costly than conventional algorithms in terms of computation. Advanced deep learning algorithms can take numerous weeks to train entirely from scratch to enable quality training of deep neural networks. By comparison, it requires less time for most traditional machine learning algorithms to practice, varying from a few minutes to a few hours or days.
Neural networks are perfect for some issues and not so good for others at the end of the day. At the moment, deep learning is a little over-buzzed and the standards surpass what can actually be accomplished with it. But that doesn’t mean it’s not beneficial. We live in a period of machine learning and technology is becoming more and more professionalized, enabling more individuals to use it to create useful goods.