Is Classical Machine Learning Dead? Why Deep Learning is the Future

Is Classical Machine Learning Dead? Why Deep Learning is the Future

Deep learning is technically a subset of machine learning algorithms. It is similar to the classical machine learning algorithms in terms of functionality, and it differs in terms of capabilities and flexibility. Here are these capabilities which make the deep learning algorithm more popular:

1. Automated feature extraction

Classical machine learning algorithms depend on an expert to intervene in learning and extracting features. This operation is called feature engineering, and it is usually a time-consuming and costly process. The advantage of deep learning algorithms is that they remove this process and automate feature engineering by learning an efficient representation of data within their hidden layers. Besides that, the features that deep learning algorithms extract are more powerful than those that human experts extract.

2. Adaptability and transferability

As mentioned, deep learning algorithms extract features automatically. This property makes the process of updating and training deep learning models far easier than classical machine learning algorithms. Moreover, with the advent of transfer learning methods for deep learning algorithms and using pre-trained deep networks, the adaptability of deep learning algorithms increased.

In fact, deep learning methods can be adapted to different tasks in the same domains far more easily than classical machine learning algorithms. 

3. Performance

The most important difference between deep learning and classical machine learning is that deep learning algorithms' performance improves as the scale of data increases. Deep learning algorithms are highly dependent on data. When the dataset is small, they do not perform well. But when the amount of data increases, the performance of these algorithms also increases. On the other hand, classical machine learning algorithms usually perform better on a small data set, but their performance is limited. See the following image to understand this difference:

The deep learning algorithms have some cons compared to classical machine learning algorithms. For example, they require much computation compared to classical machine learning. Moreover, although their performance on the prediction tasks is high, they are often not interpretable. 

In summary, both deep learning and machine learning algorithms have pros and cons. These pros and cons caused our approach to be different for different tasks. But due to the increase in the amount of data collected and the progress of our computational power, deep learning algorithms have become more useful in different tasks. For more insightful information on Deep Learning, immerse yourself in our Fundamentals of Deep Learning Online Training.