Why do we need deep Learning?

In the last few decades, AI has gained significant attention. Even in our daily lives, numerous applications of AI can be seen. following up with all the latest advancements in the field of AI may seem overwhelming. However, eventually it can be filtered down to 2 thing 1) machine learning and 2) Deep learning. But more recently, when trained with a large amount of data, Deep Learning is gaining a lot of attention thanks to its dominance in terms of accuracy.

Peculiar Features of deep learning:

  • A major benefit of deep learning, and a key component in understanding why it is becoming popular, is that it is powered by vast amounts of data. Most of the implemented features need to be defined by a domain specialist in standard machine learning methods in order to reduce the sophistication of the data and make patterns more apparent for working with learning algorithms. the greatest benefit of Deep Learning algorithms is that they strive to learn high-level features from data in a gradual way. This removes the need for domain knowledge and the extraction of hard core features.
  • The problem solving approach is another major distinction between deep learning and machine learning strategies. Deep learning techniques aim to solve the problem end to end, where the problem statements need to be broken down to different pieces to be solved first as machine learning techniques and then their outputs need to be merged at the final level.


  • Usually, due to large numbers of parameters, a Deep Learning algorithm takes a long time to train. It may take about two weeks to train entirely from scratch with the popular ResNet algorithm. Whereas, it takes a few seconds to a few hours to train classical machine learning algorithms. In the testing stage, the scenario is completely reversed. The Deep Learning algorithm takes even less time to run during test time.

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Key reasons to use deep learning:

  • If the data size is high, Deep Learning Out performs other techniques. However, standard Machine Learning algorithms are superior to limited data sizes.
  • Techniques for deep learning ought to have high-end resources in order to be trained in a decent period of time.
  • When it comes to complex problems such as image classification, natural language processing, and voice recognition, Deep Learning really outperforms all the other methodologies.

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Ashpreet Kaur - Jul 2, 2021

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