Advantages on Deep Learning over Machine Learning

 Deep Learning was developed as a Machine Learning approach to deal with complex input-output mappings. While traditional ML methods successfully solve problems where final value is a simple function of input data. On the contrary, Deep Learning techniques are able to capture composite relations between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices.

Basic definition of Deep Learning is a set of ML techniques that use stacked layers of transformation trainable from the beginning to the end. Performance is the main key difference between both algorithms. Although, when the data is small, Deep Learning algorithms don’t perform well. This is the only reason Deep Learning algorithms need a large amount of data to understand it perfectly.

 Deep Learning is discovered and proves to have the best techniques with state-of-the-art performances. Thus, Deep Learning is surprising us and will continue to do so in the near future. Recently, researchers are continuous in exploring Machine Learning and Deep Learning. In the past, researchers were limited to academia. But, nowadays, research in ML and Deep Learning is making their place in both industries and academia.

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