With machine learning, to perform any task, we need to design the right set of features and feed those features to the machine learning model. Feature engineering is a vital task for the success of any machine learning model. But it is hard to engineer the right set of features when dealing with unstructured data like text and images. In those cases, we can use deep learning.

  With deep learning, we are not required to engineer the features since the deep neural network consists of several numbers of hidden layers. It implicitly learns and extracts the right set of features by itself. So, we don’t have to perform feature engineering by ourselves. Thus deep learning is widely used in the task where it is hard to perform feature engineering such as image recognition, text classification, and so on. Thus, in this way, deep learning differs from machine learning.
The artificial neural network consists of one input, N number of hidden, and one output layer. When the artificial neural network consists of a large number of hidden layers then it is often called the deep neural network.
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