Deep Learning is a subset of Machine Learning, which on the other hand is a subset of Artificial Intelligence
Conventional machine learning methods tend to succumb to environmental changes whereas deep learning adapts to these changes by constant feedback and improving the model. Deep learning is facilitated by neural networks which mimic the neurons in the human brain and embed multiple-layer architecture (few visible and few hidden)
For the majority of machine learning algorithms, it’s difficult to analyze unstructured data, which means it’s remaining unutilized and this is exactly where deep learning becomes useful
One of the biggest advantages of using a deep learning approach is its ability to execute feature engineering by itself.
Humans get hungry or tired and sometimes make careless mistakes. When it comes to neural networks, this isn’t the case.
Data labeling can be an expensive and time-consuming job. With a deep learning approach, the need for well-labeled data becomes obsolete as the algorithms excel at learning without any guideline.
The transfer learning approach is being used by most deep learning. It's a process which involves fine-tuning a pre-trained model.
In order to train a deep learning network from scratch, you’d need to capture a very large labeled dataset apart from designing a network architecture which will learn the features and mimic.
It’s a more specialized, slightly less common approach to deep learning where the network is used as a feature extractor.